From daaf833d8635a411db3dd8b06f781d8949823cb3 Mon Sep 17 00:00:00 2001 From: Kevin Date: Sun, 24 Apr 2022 21:19:59 -0400 Subject: [PATCH 01/37] Initial commit --- motionDistortion.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/motionDistortion.py b/motionDistortion.py index b668b07..8f54e86 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -70,9 +70,9 @@ def compute_time_deltas(self): points = self.undistort()#self.p_jt # provide in N x 3 x = points[:, 0] y = points[:, 1] - angles = np.arctan2(-y, x) # scanline starts at positive x axis and moves clockwise + angles = np.arctan2(y, -x) # scanline starts at positive x axis and moves clockwise, we take midpoint pi/2 as 0 dT = self.total_scan_time * angles / (2 * np.pi) - dT -= self.total_scan_time / 2 # offset range, as defined in [-scan_time /2 , scan_time/2] + #dT -= self.total_scan_time / 2 # offset range, as defined in [-scan_time /2 , scan_time/2] self.dT = dT def undistort(self, v_j): From 67884c102dc922da4593a7ab9c4b5bfe324bb886 Mon Sep 17 00:00:00 2001 From: Kevin Date: Mon, 25 Apr 2022 03:51:31 -0400 Subject: [PATCH 02/37] Some work on the motion distort testing --- genFakeData.py | 39 ++++++++++++++++++++++----------------- getTransformKLT.py | 9 +++++---- motionDistortion.py | 6 +++--- utils.py | 11 +++++++++++ 4 files changed, 41 insertions(+), 24 deletions(-) diff --git a/genFakeData.py b/genFakeData.py index 1477d18..7d88997 100644 --- a/genFakeData.py +++ b/genFakeData.py @@ -1,6 +1,6 @@ import numpy as np import matplotlib.pyplot as plt -from utils import getRotationMatrix +from utils import getRotationMatrix, invert_transform from parseData import * from parseData import RANGE_RESOLUTION_CART_M @@ -40,7 +40,7 @@ def plotFakeFeatures(srcCoord, color='blue', marker='.', alpha=alpha, - label=f'Features 0{title_append}') + label=f'Instantaneous Radar Scan{title_append}') if targetCoord is not None: plt.scatter(targetCoord[:, 0], @@ -48,7 +48,7 @@ def plotFakeFeatures(srcCoord, color='red', marker='+', alpha=alpha, - label=f'Features 1{title_append}') + label=f'Scan with Distortion{title_append}') if targetCoord2 is not None: plt.scatter(targetCoord2[:, 0], @@ -56,7 +56,7 @@ def plotFakeFeatures(srcCoord, color='green', marker='x', alpha=alpha, - label=f'Features 2{title_append}') + label=f'Original Points{title_append}') if plotDisplace: for i in range(targetCoord.shape[0]): @@ -116,39 +116,44 @@ def convertPolarPointsToCartesian(points): y = np.expand_dims(ranges * np.sin(angles), axis = 1) return np.hstack((x, y)) -def generateFakeCorrespondencesPolar(srcCoord=None, +def generateFakeCorrespondencesPolar(currentFrame=None, n_points=100, theta_max_deg=20, max_translation_m=3): ''' @brief Generate fake correspondences with transform, randomly generated from max range and degree - @param[in] srcCoord Source coordinate to transform from. If none, will randomly generate features - @param[in] n_points Number of points to generate, only applies if srcCoord = None + @param[in] currentFrame Source coordinate to transform from. If none, will randomly generate features + @param[in] n_points Number of points to generate, only applies if currentFrame = None @param[in] theta_max_deg Maximum degree of rotation @param[in] max_range_m Maximum range (for translation) in meters - @return srcCoord Generated or passed in srcCoord + @return currentFrame Generated or passed in currentFrame @return targetCoord Corresponding targetCoord generated using (theta_deg, h) @return theta_deg Theta component of transform @return h Translation component of transform ''' - if srcCoord is None: + if currentFrame is None: print("Generating fake features..") max_range_m = max_translation_m * 3 - srcCoord = generateFakeFeaturesPolar(n_points, max_range_m) - #print(srcCoord.shape) - srcCoord = convertPolarPointsToCartesian(srcCoord) + currentFrame = generateFakeFeaturesPolar(n_points, max_range_m) + #print(currentFrame.shape) + currentFrame = convertPolarPointsToCartesian(currentFrame) else: - n_points = srcCoord.shape[0] + n_points = currentFrame.shape[0] theta_deg = np.random.random() * theta_max_deg R = getRotationMatrix(theta_deg, degrees=True) h = generateTranslationVector(max_translation_m) - #print(srcCoord.shape) - targetCoord = transformCoords(srcCoord, R, h) - - return srcCoord, targetCoord, theta_deg, h + transform = np.block([[R, h], + [np.zeros((2,)), 1]]) + T_inv = invert_transform(transform) + R_inv = T_inv[:2, :2] + h_inv = T_inv[:2, 2:] + #print(currentFrame.shape) + groundTruth = transformCoords(currentFrame, R_inv, h_inv) + + return groundTruth, currentFrame, theta_deg, h def distort(coords, velocity, frequency, h): diff --git a/getTransformKLT.py b/getTransformKLT.py index af73c65..45e914e 100644 --- a/getTransformKLT.py +++ b/getTransformKLT.py @@ -468,15 +468,16 @@ def getTrackedPointsKLT( good_old, good_new = rejectOutliers(good_old, good_new) # Obtain transforms - R, h = calculateTransformDxDth(good_old, good_new) - # R, h = calculateTransformSVD(good_old, good_new) + #R, h = calculateTransformDxDth(good_old, good_new) + R, h = calculateTransformSVD(good_old, good_new) # print(h) # h[0] += 0 # for i in range(good_old.shape[0]): # plotFakeFeatures(good_old[i:i+1,:], (R @ good_new[i:i+1,:].T + h).T, show= True) - # transformed_pts = (R @ good_new.T + h).T + transformed_pts = (R @ good_new.T + h).T # print(f"RMSE = {np.sum(np.square(good_old - transformed_pts))}") - # plotFakeFeatures(good_old, transformed_pts, good_new, show = True) + #plotFakeFeatures(good_old, good_new, show = True) + plotFakeFeatures(good_old, transformed_pts, good_new, show = True) h *= RANGE_RESOLUTION_CART_M #R, h = estimateTransformUsingDelats(good_old, good_new) diff --git a/motionDistortion.py b/motionDistortion.py index 8f54e86..02a5b81 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -57,7 +57,7 @@ def __init__(self, T_wj0, p_w, p_jt, v_j0, T_wj, lambda_p, lambda_v): self.total_scan_time = 1 / 4 # assuming 4 Hz pass - def compute_time_deltas(self): + def compute_time_deltas(self, points): ''' Get the time deltas for each point. This depends solely on where the points are in scan angle. The further away from center, the greater the @@ -67,7 +67,7 @@ def compute_time_deltas(self): idea to re-run this function once an undistorted frame is obtained for better estimates. ''' - points = self.undistort()#self.p_jt # provide in N x 3 + #points = self.undistort()#self.p_jt # provide in N x 3 x = points[:, 0] y = points[:, 1] angles = np.arctan2(y, -x) # scanline starts at positive x axis and moves clockwise, we take midpoint pi/2 as 0 @@ -81,7 +81,7 @@ def undistort(self, v_j): best estimate of v_T, T_wj, dT ''' displacement = np.expand_dims(v_j, axis = 1) * self.dT # 3 x N - assert(displacement.shape = (3,points.shape[0])) + #assert(displacement.shape == (3,points.shape[0])) theta = displacement[2, :] dx = displacement[0, :] dy = displacement[1, :] diff --git a/utils.py b/utils.py index 6b60797..8aa02f6 100644 --- a/utils.py +++ b/utils.py @@ -143,3 +143,14 @@ def plt_savefig_by_axis(filePath, fig, ax, pad=0.0): extent = plt_full_extent(ax, pad).transformed(fig.dpi_scale_trans.inverted()) # extent = ax.get_tightbbox(fig.canvas.get_renderer()) fig.savefig(filePath, bbox_inches=extent) + +def invert_transform(T): + theta = np.arctan2(T[1, 0], T[0, 0]) + x = T[0, 2] + y = T[1, 2] + c = np.cos(theta) + s = np.sin(theta) + T_inv = np.array([[ c, s, -s * y - c * x], + [-s, c, -c * y + s * x], + [ 0, 0, 1]]) + return T_inv \ No newline at end of file From 499f3dfd344b4db39f3dfe8f8d1377dd44479afc Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sat, 30 Apr 2022 01:39:48 -0400 Subject: [PATCH 03/37] Update interface for mapping --- Mapping.py | 13 ++++++++----- RawROAMSystem.py | 5 ++++- Tracker.py | 7 +++++-- outlierRejection.py | 10 +++++----- 4 files changed, 22 insertions(+), 13 deletions(-) diff --git a/Mapping.py b/Mapping.py index 4af5e32..03aebbe 100644 --- a/Mapping.py +++ b/Mapping.py @@ -15,15 +15,15 @@ # Keyframe class class Keyframe(): - def __init__(self, pose: np.ndarray, featurePoints: np.ndarray, + def __init__(self, globalPose: np.ndarray, featurePoints: np.ndarray, radarPolarImg: np.ndarray) -> None: ''' @brief Keyframe class. Contains pose, feature points and point cloud information - @param[in] pose (3 x 1) Pose information [x, y, th] in (m, m, rad) # TODO: Confirm these units - @param[in] featurePoints (K x 2) Tracked feature points from previous keyframe - @param[in] radarPolarImg (M x N) Radar polar (range-azimuth) image + @param[in] globalPose (3 x 1) Global pose information [x, y, th] in (m, m, rad) # TODO: Confirm these units + @param[in] featurePoints (K x 2) Tracked feature points from previous keyframe + @param[in] radarPolarImg (M x N) Radar polar (range-azimuth) image ''' - self.pose = pose + self.pose = globalPose self.featurePoints = featurePoints # set of (tracked) feature points self.radarPolarImg = radarPolarImg # radar polar image @@ -53,6 +53,9 @@ def __init__(self, sequenceName: str, estTraj: Trajectory, self.estTraj = estTraj self.keyframes = [] + def updateInternalTraj(self, traj: Trajectory): + self.estTraj = traj + # TODO: might not want to make keyframe before adding it def isGoodKeyframe(self, keyframe: Keyframe) -> bool: ''' diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 8cd71db..751b00c 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -3,6 +3,7 @@ from matplotlib import pyplot as plt import numpy as np +from Mapping import Map from getFeatures import appendNewFeatures from parseData import convertPolarImageToCartesian, getCartImageFromImgPaths, getPolarImageFromImgPaths, getRadarImgPaths from trajectoryPlotting import Trajectory, getGroundTruthTrajectory, plotGtAndEstTrajectory @@ -73,6 +74,8 @@ def __init__(self, self.filePaths, self.paramFlags) # TODO: Initialize mapping + self.map = Map(self.sequenceName, self.estTraj, self.imgPathArr, + self.filePaths) pass @@ -136,7 +139,7 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: currImgCart = convertPolarImageToCartesian(currImgPolar) # Perform tracking - good_old, good_new, rotAngleRad = tracker.track( + good_old, good_new, rotAngleRad, corrStatus = tracker.track( prevImgCart, currImgCart, prevImgPolar, currImgPolar, blobCoord, seqInd) print("Detected", np.rad2deg(rotAngleRad), "[deg] rotation") diff --git a/Tracker.py b/Tracker.py index dfe1ddb..5d78437 100644 --- a/Tracker.py +++ b/Tracker.py @@ -52,6 +52,7 @@ def track( @return good_old Coordinates of old good feature points (K' x 2) in [x, y] format @return good_new Coordinates of new good feature points (K' x 2) in [x, y] format @return angleRotRad Angle used to rotate image + @return corrStatus (K x 2) correspondence status @note Needed for mapping to track keyframe points ''' # Timing start = tic() @@ -84,9 +85,11 @@ def track( # Outlier rejection doOutlierRejection = self.paramFlags.get("rejectOutliers", True) if doOutlierRejection: - good_old, good_new = rejectOutliers(good_old, good_new) + good_old, good_new, pruning_mask = rejectOutliers(good_old, good_new) - return good_old, good_new, angleRotRad + corrStatus &= pruning_mask + + return good_old, good_new, angleRotRad, corrStatus def getTransform(self, srcCoord: np.ndarray, targetCoord: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: diff --git a/outlierRejection.py b/outlierRejection.py index 45722d1..548fb23 100644 --- a/outlierRejection.py +++ b/outlierRejection.py @@ -15,9 +15,8 @@ def rejectOutliers( prev_coord: np.ndarray, - new_coord: np.ndarray, - # outlierInd=None -) -> tuple[np.ndarray, np.ndarray]: + new_coord: np.ndarray +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: ''' @brief Reject outliers by using radar geometry to find dynamic/moving features @@ -37,6 +36,7 @@ def rejectOutliers( @return pruned_prev_coord (k x 2) Pruned previous coordinates @return pruned_new_coord (k x 2) Pruned current/new coordinates + @return pruning_mask (K x 2) Pruning mask ''' assert prev_coord.shape == new_coord.shape, "Coordinates should be the same shape" @@ -56,7 +56,7 @@ def rejectOutliers( distDiff = np.abs(dist_prev - dist_new) distThreshMask = (distDiff <= DIST_THRESHOLD_PX).astype(np.int8) - + print("Mean Distance Error:", distDiff.mean(), "[px] | Threshold:", DIST_THRESHOLD_PX, "[px]") # Form graph using the distThreshMask matrix @@ -92,7 +92,7 @@ def rejectOutliers( pruned_prev_coord = prev_coord[pruning_mask] pruned_new_coord = new_coord[pruning_mask] - return pruned_prev_coord, pruned_new_coord + return pruned_prev_coord, pruned_new_coord, pruning_mask if __name__ == "__main__": From 308e81457a23a4315db2945ae12b2107e5339aba Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sat, 30 Apr 2022 02:03:41 -0400 Subject: [PATCH 04/37] conversion to global coordinates --- Mapping.py | 67 +++++++++++++++++++++++++++++++++++++++++++----- RawROAMSystem.py | 5 +++- 2 files changed, 64 insertions(+), 8 deletions(-) diff --git a/Mapping.py b/Mapping.py index 03aebbe..4def790 100644 --- a/Mapping.py +++ b/Mapping.py @@ -1,35 +1,86 @@ import numpy as np import scipy as sp -from parseData import MAX_RANGE_CLIP_DEFAULT +from parseData import MAX_RANGE_CLIP_DEFAULT, RANGE_RESOLUTION_CART_M, convertPolarImageToCartesian from trajectoryPlotting import Trajectory -import m2dp +# import m2dp from getPointCloud import getPointCloudPolarInd +from utils import getRotationMatrix # Thresholds ROT_THRESHOLD = 0.2 # radians TRANS_THRESHOLD = 2.0 # meters TRANS_THRESHOLD_SQ = TRANS_THRESHOLD * TRANS_THRESHOLD # meters^2 +RADAR_CART_CENTER = None + # Keyframe class class Keyframe(): - def __init__(self, globalPose: np.ndarray, featurePoints: np.ndarray, + def __init__(self, globalPose: np.ndarray, featurePointsLocal: np.ndarray, radarPolarImg: np.ndarray) -> None: ''' @brief Keyframe class. Contains pose, feature points and point cloud information - @param[in] globalPose (3 x 1) Global pose information [x, y, th] in (m, m, rad) # TODO: Confirm these units - @param[in] featurePoints (K x 2) Tracked feature points from previous keyframe - @param[in] radarPolarImg (M x N) Radar polar (range-azimuth) image + @param[in] globalPose (3 x 1) Pose information [x, y, th] in global coordinates, + units of [m, m, rad] # TODO: Confirm these units + @param[in] featurePointsLocal (K x 2) Tracked feature points from previous keyframe, + in local coordinates (pixels) + @param[in] radarPolarImg (M x N) Radar polar (range-azimuth) image ''' self.pose = globalPose - self.featurePoints = featurePoints # set of (tracked) feature points self.radarPolarImg = radarPolarImg # radar polar image + print(self.pose) + + # Figure out and cache the center of the Cartesian image, needed for converting coordinates + global RADAR_CART_CENTER + if RADAR_CART_CENTER is None: + radarCartImg = convertPolarImageToCartesian(radarPolarImg) + RADAR_CART_CENTER = np.array(radarCartImg.shape) / 2 + + # NOTE: self.pose should be set before this + # Creates sets of feature points in global coordinate, meters + featurePointsGlobal = self.convertFeaturesLocalToGlobal( + featurePointsLocal) + + self.featurePoints = featurePointsGlobal # set of original feature points + self.prunedFeaturePoints = self.featurePoints # set of pruned feature points (tracked until the next keyframe) + # TODO: Not sure if needed/useful self.pointCloud = getPointCloudPolarInd(radarPolarImg) + def convertFeaturesLocalToGlobal( + self, featurePointsLocal: np.ndarray) -> np.ndarray: + ''' + @brief Local feature points to convert into global coordinates, given internal pose + @param[in] featurePointsLocal (K x 2) Tracked feature points from previous keyframe, + in local coordinates (pixels) + @return featurePointsGlobal (K x 2) Feature points in global coordinates, meters + ''' + x, y, th = self.pose + + # First translate local points to origin at center + featurePointsGlobal = featurePointsLocal - RADAR_CART_CENTER + + # Then we need to convert to meters + featurePointsGlobal *= RANGE_RESOLUTION_CART_M # px * (m/px) = m + + # Rotate and translate to make into global coordinate system + R = getRotationMatrix(th) + t = np.array([x, y]).reshape(2, 1) + featurePointsGlobal = (R @ featurePointsGlobal.T + t).T + + return featurePointsGlobal + + def pruneFeaturePoints(self, corrStatus: np.ndarray) -> None: + ''' + @brief Prune feature points based on correspondence status + @param[in] corrStatus + @note In place changing of `self.prunedFeaturePoints` function, which aims to track and prune away the feature points that are part of this keyframe + ''' + self.prunedFeaturePoints = self.prunedFeaturePoints[corrStatus] + # def isVisible(self, keyframe): # ''' # @brief Return points that are visible from keyframe @@ -51,6 +102,8 @@ def __init__(self, sequenceName: str, estTraj: Trajectory, self.filePaths = filePaths self.estTraj = estTraj + + # TODO: Instead of storing set of all keyframes, only store the last keyframe, and then a big array of map points in global coordinates self.keyframes = [] def updateInternalTraj(self, traj: Trajectory): diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 751b00c..00f951a 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -3,7 +3,7 @@ from matplotlib import pyplot as plt import numpy as np -from Mapping import Map +from Mapping import Keyframe, Map from getFeatures import appendNewFeatures from parseData import convertPolarImageToCartesian, getCartImageFromImgPaths, getPolarImageFromImgPaths, getRadarImgPaths from trajectoryPlotting import Trajectory, getGroundTruthTrajectory, plotGtAndEstTrajectory @@ -133,6 +133,9 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: blobCoord = np.empty((0, 2)) blobCoord, _ = appendNewFeatures(prevImgCart, blobCoord) + # Initialize first keyframe + kf = Keyframe(initPose, blobCoord, prevImgPolar) + for seqInd in range(startSeqInd + 1, endSeqInd + 1): # Obtain polar and Cart image currImgPolar = getPolarImageFromImgPaths(imgPathArr, seqInd) From 5b47c8a65a48f09d165a253c81b1c777818fd5c8 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sat, 30 Apr 2022 02:04:05 -0400 Subject: [PATCH 05/37] remove old comments --- Mapping.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/Mapping.py b/Mapping.py index 4def790..bd11fe7 100644 --- a/Mapping.py +++ b/Mapping.py @@ -31,8 +31,6 @@ def __init__(self, globalPose: np.ndarray, featurePointsLocal: np.ndarray, self.pose = globalPose self.radarPolarImg = radarPolarImg # radar polar image - print(self.pose) - # Figure out and cache the center of the Cartesian image, needed for converting coordinates global RADAR_CART_CENTER if RADAR_CART_CENTER is None: From 6ce93bd8b29c10ea49a3ede37cca1576dd16beae Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sat, 30 Apr 2022 02:30:01 -0400 Subject: [PATCH 06/37] basic interface for adding keyframes --- Mapping.py | 30 ++++++++++++++++++++++-------- RawROAMSystem.py | 20 ++++++++++++++++++-- 2 files changed, 40 insertions(+), 10 deletions(-) diff --git a/Mapping.py b/Mapping.py index bd11fe7..cb1d68d 100644 --- a/Mapping.py +++ b/Mapping.py @@ -22,6 +22,21 @@ def __init__(self, globalPose: np.ndarray, featurePointsLocal: np.ndarray, radarPolarImg: np.ndarray) -> None: ''' @brief Keyframe class. Contains pose, feature points and point cloud information + @param[in] globalPose (3 x 1) Pose information [x, y, th] in global coordinates, + units of [m, m, rad] # TODO: Confirm these units + @param[in] featurePointsLocal (K x 2) Tracked feature points from previous keyframe, + in local coordinates (pixels) + @param[in] radarPolarImg (M x N) Radar polar (range-azimuth) image + + @see updateInfo() + ''' + self.updateInfo(globalPose, featurePointsLocal, radarPolarImg) + + def updateInfo(self, globalPose: np.ndarray, + featurePointsLocal: np.ndarray, + radarPolarImg: np.ndarray) -> None: + ''' + @brief Update internal information: pose, feature points and point cloud information @param[in] globalPose (3 x 1) Pose information [x, y, th] in global coordinates, units of [m, m, rad] # TODO: Confirm these units @param[in] featurePointsLocal (K x 2) Tracked feature points from previous keyframe, @@ -37,17 +52,16 @@ def __init__(self, globalPose: np.ndarray, featurePointsLocal: np.ndarray, radarCartImg = convertPolarImageToCartesian(radarPolarImg) RADAR_CART_CENTER = np.array(radarCartImg.shape) / 2 - # NOTE: self.pose should be set before this - # Creates sets of feature points in global coordinate, meters - featurePointsGlobal = self.convertFeaturesLocalToGlobal( - featurePointsLocal) - - self.featurePoints = featurePointsGlobal # set of original feature points - self.prunedFeaturePoints = self.featurePoints # set of pruned feature points (tracked until the next keyframe) + self.featurePointsLocal = featurePointsLocal # set of original feature points, in local (px coordinates) + self.prunedFeaturePoints = self.featurePointsLocal # set of pruned feature points (tracked until the next keyframe) # TODO: Not sure if needed/useful self.pointCloud = getPointCloudPolarInd(radarPolarImg) + def copyFromOtherKeyframe(self, keyframe) -> None: + self.updateInfo(keyframe.globalPose, keyframe.featurePointsLocal, + keyframe.radarPolarImg) + def convertFeaturesLocalToGlobal( self, featurePointsLocal: np.ndarray) -> np.ndarray: ''' @@ -62,7 +76,7 @@ def convertFeaturesLocalToGlobal( featurePointsGlobal = featurePointsLocal - RADAR_CART_CENTER # Then we need to convert to meters - featurePointsGlobal *= RANGE_RESOLUTION_CART_M # px * (m/px) = m + featurePointsGlobal *= RANGE_RESOLUTION_CART_M # px * (m/px) = m # Rotate and translate to make into global coordinate system R = getRotationMatrix(th) diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 00f951a..dec2cf0 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -1,8 +1,8 @@ import os import shutil from matplotlib import pyplot as plt - import numpy as np + from Mapping import Keyframe, Map from getFeatures import appendNewFeatures from parseData import convertPolarImageToCartesian, getCartImageFromImgPaths, getPolarImageFromImgPaths, getRadarImgPaths @@ -134,7 +134,10 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: blobCoord, _ = appendNewFeatures(prevImgCart, blobCoord) # Initialize first keyframe - kf = Keyframe(initPose, blobCoord, prevImgPolar) + possible_kf = Keyframe(initPose, blobCoord, prevImgPolar) + self.map.addKeyframe(possible_kf) + + old_kf.copyFromOtherKeyframe(possible_kf) for seqInd in range(startSeqInd + 1, endSeqInd + 1): # Obtain polar and Cart image @@ -142,9 +145,11 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: currImgCart = convertPolarImageToCartesian(currImgPolar) # Perform tracking + # TODO: Figure out how to integrate the keyframe addition when creation of new features good_old, good_new, rotAngleRad, corrStatus = tracker.track( prevImgCart, currImgCart, prevImgPolar, currImgPolar, blobCoord, seqInd) + print("Detected", np.rad2deg(rotAngleRad), "[deg] rotation") estR = getRotationMatrix(-rotAngleRad) @@ -155,6 +160,17 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # Update trajectory self.updateTrajectory(R, h, seqInd) + # Keyframe updating + old_kf.pruneFeaturePoints(corrStatus) + + latestPose = self.estTraj.poses[-1] + possible_kf.updateInfo(latestPose, good_new, currImgPolar) + + # TODO: Check if keyframe needs to be added to the map + if self.map.isGoodKeyframe(possible_kf): + self.map.addKeyframe(possible_kf) + old_kf.copyFromOtherKeyframe(possible_kf) + # Plotting and prints and stuff self.plot(prevImgCart, currImgCart, good_old, good_new, R, h, seqInd) From 1e8179442249cfd4a417d7480c1a6f47b4c91856 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sat, 30 Apr 2022 02:34:49 -0400 Subject: [PATCH 07/37] Added basic skeleton for bundle adjustment --- Mapping.py | 20 ++++++++++++++++++++ RawROAMSystem.py | 5 ++++- 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/Mapping.py b/Mapping.py index cb1d68d..46c314f 100644 --- a/Mapping.py +++ b/Mapping.py @@ -162,4 +162,24 @@ def bundleAdjustment(self) -> None: @brief Perform bundle adjustment on the last 2 keyframes @return None ''' + # Cannot do BA on only 1 KF + if len(self.keyframes) <= 1: + return + + # TODO: Perform bundle adjustment on last 2 keyframes + old_kf = self.keyframes[-2] + new_kf = self.keyframes[-1] + + # Obtain relevant information + pose_old = old_kf.pose + pose_new = new_kf.pose + points_old_local = old_kf.featurePointsLocal + points_new_local = new_kf.featurePointsLocal + + # NOTE: remember ot convert to global coordinates before doing anything with the keyframes + points_old = old_kf.convert(points_old_local) + points_new = new_kf.convert(points_new_local) + + # TODO: iterative optimisation and related solvers here + pass \ No newline at end of file diff --git a/RawROAMSystem.py b/RawROAMSystem.py index dec2cf0..a6eb297 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -137,7 +137,7 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: possible_kf = Keyframe(initPose, blobCoord, prevImgPolar) self.map.addKeyframe(possible_kf) - old_kf.copyFromOtherKeyframe(possible_kf) + old_kf = Keyframe(initPose, blobCoord, prevImgPolar) for seqInd in range(startSeqInd + 1, endSeqInd + 1): # Obtain polar and Cart image @@ -171,6 +171,9 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: self.map.addKeyframe(possible_kf) old_kf.copyFromOtherKeyframe(possible_kf) + # TODO: do bundle adjustment here + self.map.bundleAdjustment() + # Plotting and prints and stuff self.plot(prevImgCart, currImgCart, good_old, good_new, R, h, seqInd) From a55300bf32eb18e5a882defca8ca96298b1a93a6 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sat, 30 Apr 2022 02:35:20 -0400 Subject: [PATCH 08/37] oops need the prunedFeaturePoints --- Mapping.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Mapping.py b/Mapping.py index 46c314f..c881abd 100644 --- a/Mapping.py +++ b/Mapping.py @@ -173,7 +173,7 @@ def bundleAdjustment(self) -> None: # Obtain relevant information pose_old = old_kf.pose pose_new = new_kf.pose - points_old_local = old_kf.featurePointsLocal + points_old_local = old_kf.prunedFeaturePoints points_new_local = new_kf.featurePointsLocal # NOTE: remember ot convert to global coordinates before doing anything with the keyframes @@ -181,5 +181,5 @@ def bundleAdjustment(self) -> None: points_new = new_kf.convert(points_new_local) # TODO: iterative optimisation and related solvers here - + pass \ No newline at end of file From 8d1632f6fc8095a9fc1cb96c9f5376cf55539d2f Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sun, 1 May 2022 00:02:56 -0400 Subject: [PATCH 09/37] random g2o update --- g2o | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/g2o b/g2o index cc40301..d1b9054 160000 --- a/g2o +++ b/g2o @@ -1 +1 @@ -Subproject commit cc403018ea05475380794f433666a442d675f0e2 +Subproject commit d1b9054491f6856e6b1c6fe5cf775df5a184a1c0 From ccc1e6efb1c0cd5d7f0a5b6229bcddf1b3c52ec8 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sun, 1 May 2022 00:08:18 -0400 Subject: [PATCH 10/37] back to master branch --- g2o | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/g2o b/g2o index d1b9054..b1ba729 160000 --- a/g2o +++ b/g2o @@ -1 +1 @@ -Subproject commit d1b9054491f6856e6b1c6fe5cf775df5a184a1c0 +Subproject commit b1ba729aa569267e179fa2e237db0b3ad5169e2e From 6b799b9961176cd6889656783afe3033ddd654f5 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sun, 1 May 2022 00:14:10 -0400 Subject: [PATCH 11/37] criteria check for not enough features --- RawROAMSystem.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/RawROAMSystem.py b/RawROAMSystem.py index a6eb297..b667fa9 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -4,7 +4,7 @@ import numpy as np from Mapping import Keyframe, Map -from getFeatures import appendNewFeatures +from getFeatures import N_FEATURES_BEFORE_RETRACK, appendNewFeatures from parseData import convertPolarImageToCartesian, getCartImageFromImgPaths, getPolarImageFromImgPaths, getRadarImgPaths from trajectoryPlotting import Trajectory, getGroundTruthTrajectory, plotGtAndEstTrajectory from utils import convertRandHtoDeltas, f_arr, getRotationMatrix, plt_savefig_by_axis, radarImgPathToTimestamp @@ -166,8 +166,17 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: latestPose = self.estTraj.poses[-1] possible_kf.updateInfo(latestPose, good_new, currImgPolar) - # TODO: Check if keyframe needs to be added to the map - if self.map.isGoodKeyframe(possible_kf): + # Add a keyframe if it fulfills criteria + # 1) large enough translation from previous keyframe + # 2) large enough rotation from previous KF + # TODO: Not sure if this criteria is actually correct + # 3) not enough features in current keyframe (ie about to have new features coming up) + # NOTE: Feature check and appending will only be checked in the next iteration, + # so we can prematuraly do it here and add a keyframe first + nFeatures = good_new.shape[0] + if (nFeatures <= N_FEATURES_BEFORE_RETRACK) or \ + self.map.isGoodKeyframe(possible_kf): + self.map.addKeyframe(possible_kf) old_kf.copyFromOtherKeyframe(possible_kf) From b7c50b046fe7af441c3092981626573421df97e6 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sun, 1 May 2022 00:14:55 -0400 Subject: [PATCH 12/37] update todo statement --- RawROAMSystem.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/RawROAMSystem.py b/RawROAMSystem.py index b667fa9..dc2b6b5 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -169,7 +169,7 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # Add a keyframe if it fulfills criteria # 1) large enough translation from previous keyframe # 2) large enough rotation from previous KF - # TODO: Not sure if this criteria is actually correct + # TODO: Not sure if this criteria is actually correct, perhaps we should be adding the previous keyframe instead # 3) not enough features in current keyframe (ie about to have new features coming up) # NOTE: Feature check and appending will only be checked in the next iteration, # so we can prematuraly do it here and add a keyframe first From 1b943e0b8703ff1cfaff2273ceedacb96cfffd09 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sun, 1 May 2022 04:25:02 -0400 Subject: [PATCH 13/37] update interface with skeleton for plotting --- Mapping.py | 11 ++++++++++- RawROAMSystem.py | 3 +++ 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/Mapping.py b/Mapping.py index c881abd..31ce404 100644 --- a/Mapping.py +++ b/Mapping.py @@ -116,6 +116,7 @@ def __init__(self, sequenceName: str, estTraj: Trajectory, self.estTraj = estTraj # TODO: Instead of storing set of all keyframes, only store the last keyframe, and then a big array of map points in global coordinates + self.mapPoints = [] # should be a large np.array of global feature points self.keyframes = [] def updateInternalTraj(self, traj: Trajectory): @@ -160,6 +161,7 @@ def addKeyframe(self, keyframe: Keyframe) -> None: def bundleAdjustment(self) -> None: ''' @brief Perform bundle adjustment on the last 2 keyframes + # TODO: Should actually bundle adjust on all keyframes within range @return None ''' # Cannot do BA on only 1 KF @@ -182,4 +184,11 @@ def bundleAdjustment(self) -> None: # TODO: iterative optimisation and related solvers here - pass \ No newline at end of file + pass + + def plot(self, fig): + ''' + @brief Plot map points on plt figure + @param[in] fig plt figure to plot on @todo do this + ''' + return \ No newline at end of file diff --git a/RawROAMSystem.py b/RawROAMSystem.py index dc2b6b5..1e8f820 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -225,6 +225,9 @@ def plot(self, ax2 = self.fig.add_subplot(1, 2, 2) self.plotTraj(seqInd, R, h, save=False, show=False) + + # TODO: Plotting for map points + self.map.plot(self.fig) trajSavePath = self.filePaths["trajSave"] trajSavePathInd = os.path.join(trajSavePath, f"{seqInd:04d}.jpg") From e0acae052088db79b8999e4c60e7f537eed9bdfb Mon Sep 17 00:00:00 2001 From: atomicapple Date: Sun, 1 May 2022 14:57:59 -0400 Subject: [PATCH 14/37] move files to archive --- Mapping.py | 2 ++ Tracker.py | 5 +++-- testDeadReckoning.py => archive/testDeadReckoning.py | 0 testTransformKLT2.py => archive/testTransformKLT2.py | 0 g2o | 2 +- getTransformKLT.py | 2 +- img/render_gif.py | 4 ++-- 7 files changed, 9 insertions(+), 6 deletions(-) rename testDeadReckoning.py => archive/testDeadReckoning.py (100%) rename testTransformKLT2.py => archive/testTransformKLT2.py (100%) diff --git a/Mapping.py b/Mapping.py index 31ce404..3d8301a 100644 --- a/Mapping.py +++ b/Mapping.py @@ -183,6 +183,8 @@ def bundleAdjustment(self) -> None: points_new = new_kf.convert(points_new_local) # TODO: iterative optimisation and related solvers here + # 2d bundle adjustment + pass diff --git a/Tracker.py b/Tracker.py index 5d78437..4f289e3 100644 --- a/Tracker.py +++ b/Tracker.py @@ -4,8 +4,8 @@ from matplotlib import pyplot as plt import numpy as np -from FMT import getRotationUsingFMT, getTranslationUsingPhaseCorrelation, rotateImg -from getTransformKLT import calculateTransformDxDth, calculateTransformSVD, getTrackedPointsKLT, visualize_transform +from FMT import getRotationUsingFMT +from getTransformKLT import calculateTransformSVD, getTrackedPointsKLT, visualize_transform from outlierRejection import rejectOutliers from parseData import RANGE_RESOLUTION_CART_M from trajectoryPlotting import Trajectory @@ -87,6 +87,7 @@ def track( if doOutlierRejection: good_old, good_new, pruning_mask = rejectOutliers(good_old, good_new) + # ???: (213,1) &= (94,) corrStatus &= pruning_mask return good_old, good_new, angleRotRad, corrStatus diff --git a/testDeadReckoning.py b/archive/testDeadReckoning.py similarity index 100% rename from testDeadReckoning.py rename to archive/testDeadReckoning.py diff --git a/testTransformKLT2.py b/archive/testTransformKLT2.py similarity index 100% rename from testTransformKLT2.py rename to archive/testTransformKLT2.py diff --git a/g2o b/g2o index b1ba729..cc40301 160000 --- a/g2o +++ b/g2o @@ -1 +1 @@ -Subproject commit b1ba729aa569267e179fa2e237db0b3ad5169e2e +Subproject commit cc403018ea05475380794f433666a442d675f0e2 diff --git a/getTransformKLT.py b/getTransformKLT.py index af73c65..39bb4c2 100644 --- a/getTransformKLT.py +++ b/getTransformKLT.py @@ -61,7 +61,7 @@ def visualize_transform(prevImg: np.ndarray, marker='.', color='yellow', alpha=alpha, - label=f'Image 0 Features{extraLabel}') + label=f'Previous Features{extraLabel}') plt.legend() plt.axis("off") diff --git a/img/render_gif.py b/img/render_gif.py index 2d6533b..d641670 100644 --- a/img/render_gif.py +++ b/img/render_gif.py @@ -5,8 +5,8 @@ # truly a windows moment images = [] for fname in sorted( - glob.glob("track_klt_thresholding/full_seq_1_traj/*.jpg"), key=lambda x: int(x.split("\\")[1].split(".")[0]) + glob.glob("roam/full_seq_1_traj/*.jpg"), key=lambda x: int(x.split("\\")[1].split(".")[0]) ): print(fname) images.append(imageio.imread(fname)) -imageio.mimsave("track_klt_thresholding/full_seq_1_traj_1.gif", images, fps=10) \ No newline at end of file +imageio.mimsave("roam/full_seq_1_traj_1.gif", images, fps=10) \ No newline at end of file From 477e5429a5d486ba204a6c85488eec9b300a6c26 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sun, 1 May 2022 16:33:33 -0400 Subject: [PATCH 15/37] Fix issue with corrStatus shape --- Tracker.py | 6 ++++-- g2o | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/Tracker.py b/Tracker.py index 4f289e3..52cb7f6 100644 --- a/Tracker.py +++ b/Tracker.py @@ -2,6 +2,7 @@ import os from typing import Tuple from matplotlib import pyplot as plt +from matplotlib.ft2font import BOLD import numpy as np from FMT import getRotationUsingFMT @@ -87,8 +88,9 @@ def track( if doOutlierRejection: good_old, good_new, pruning_mask = rejectOutliers(good_old, good_new) - # ???: (213,1) &= (94,) - corrStatus &= pruning_mask + # Ensure correct correspondence status + rng = np.arange(nFeatures) + corrStatus[rng[corrStatus.flatten().astype(bool)]] &= pruning_mask[:, np.newaxis] return good_old, good_new, angleRotRad, corrStatus diff --git a/g2o b/g2o index cc40301..b1ba729 160000 --- a/g2o +++ b/g2o @@ -1 +1 @@ -Subproject commit cc403018ea05475380794f433666a442d675f0e2 +Subproject commit b1ba729aa569267e179fa2e237db0b3ad5169e2e From 153e734a67d6563eadd408e331ff7c837deaec70 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sun, 1 May 2022 16:40:40 -0400 Subject: [PATCH 16/37] Attempt at plotting map --- Mapping.py | 20 +++++++++++++++----- RawROAMSystem.py | 4 ++-- 2 files changed, 17 insertions(+), 7 deletions(-) diff --git a/Mapping.py b/Mapping.py index 3d8301a..f52bfc9 100644 --- a/Mapping.py +++ b/Mapping.py @@ -1,3 +1,4 @@ +from matplotlib import pyplot as plt import numpy as np import scipy as sp @@ -116,7 +117,8 @@ def __init__(self, sequenceName: str, estTraj: Trajectory, self.estTraj = estTraj # TODO: Instead of storing set of all keyframes, only store the last keyframe, and then a big array of map points in global coordinates - self.mapPoints = [] # should be a large np.array of global feature points + self.mapPoints = [ + ] # should be a large np.array of global feature points self.keyframes = [] def updateInternalTraj(self, traj: Trajectory): @@ -179,18 +181,26 @@ def bundleAdjustment(self) -> None: points_new_local = new_kf.featurePointsLocal # NOTE: remember ot convert to global coordinates before doing anything with the keyframes - points_old = old_kf.convert(points_old_local) - points_new = new_kf.convert(points_new_local) + points_old = old_kf.convertFeaturesLocalToGlobal(points_old_local) + points_new = new_kf.convertFeaturesLocalToGlobal(points_new_local) # TODO: iterative optimisation and related solvers here # 2d bundle adjustment - pass - def plot(self, fig): + def plot(self, fig: plt.figure, show: bool = False): ''' @brief Plot map points on plt figure @param[in] fig plt figure to plot on @todo do this ''' + # TODO: For now, plot all the keyframe feature points + for kf in self.keyframes: + points_local = kf.featurePointsLocal + points_global = kf.convertFeaturesLocalToGlobal(points_local) + plt.plot(points_global) + + if (show): + plt.show() + return \ No newline at end of file diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 1e8f820..478074e 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -47,7 +47,7 @@ def __init__(self, self.sequenceSize = len(self.imgPathArr) # Create Save paths for imaging - imgSavePath = os.path.join(".", "img", "roam_rejectOutliers_1m", + imgSavePath = os.path.join(".", "img", "roam_mapping", sequenceName).strip(os.path.sep) trajSavePath = imgSavePath + '_traj' @@ -227,7 +227,7 @@ def plot(self, self.plotTraj(seqInd, R, h, save=False, show=False) # TODO: Plotting for map points - self.map.plot(self.fig) + self.map.plot(self.fig, show=False) trajSavePath = self.filePaths["trajSave"] trajSavePathInd = os.path.join(trajSavePath, f"{seqInd:04d}.jpg") From 9da2c012f68bbfdb76d9d3d29cc3f1be94a3243c Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sun, 1 May 2022 16:46:20 -0400 Subject: [PATCH 17/37] Fix issues with keyframe aliasing --- Mapping.py | 2 +- RawROAMSystem.py | 12 +++++++----- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/Mapping.py b/Mapping.py index f52bfc9..2a06a38 100644 --- a/Mapping.py +++ b/Mapping.py @@ -60,7 +60,7 @@ def updateInfo(self, globalPose: np.ndarray, self.pointCloud = getPointCloudPolarInd(radarPolarImg) def copyFromOtherKeyframe(self, keyframe) -> None: - self.updateInfo(keyframe.globalPose, keyframe.featurePointsLocal, + self.updateInfo(keyframe.pose, keyframe.featurePointsLocal, keyframe.radarPolarImg) def convertFeaturesLocalToGlobal( diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 478074e..8daba7f 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -134,10 +134,10 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: blobCoord, _ = appendNewFeatures(prevImgCart, blobCoord) # Initialize first keyframe - possible_kf = Keyframe(initPose, blobCoord, prevImgPolar) - self.map.addKeyframe(possible_kf) - old_kf = Keyframe(initPose, blobCoord, prevImgPolar) + self.map.addKeyframe(old_kf) + + possible_kf = Keyframe(initPose, blobCoord, prevImgPolar) for seqInd in range(startSeqInd + 1, endSeqInd + 1): # Obtain polar and Cart image @@ -177,8 +177,10 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: if (nFeatures <= N_FEATURES_BEFORE_RETRACK) or \ self.map.isGoodKeyframe(possible_kf): - self.map.addKeyframe(possible_kf) + print("\nAdding keyframe...\n") + old_kf.copyFromOtherKeyframe(possible_kf) + self.map.addKeyframe(old_kf) # TODO: do bundle adjustment here self.map.bundleAdjustment() @@ -225,7 +227,7 @@ def plot(self, ax2 = self.fig.add_subplot(1, 2, 2) self.plotTraj(seqInd, R, h, save=False, show=False) - + # TODO: Plotting for map points self.map.plot(self.fig, show=False) From 6ee36efcdc5dd9657f4944759aac5654c1d69852 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Sun, 1 May 2022 16:54:46 -0400 Subject: [PATCH 18/37] Still some issues with plotting --- Mapping.py | 5 +++-- RawROAMSystem.py | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/Mapping.py b/Mapping.py index 2a06a38..59409dc 100644 --- a/Mapping.py +++ b/Mapping.py @@ -198,9 +198,10 @@ def plot(self, fig: plt.figure, show: bool = False): for kf in self.keyframes: points_local = kf.featurePointsLocal points_global = kf.convertFeaturesLocalToGlobal(points_local) - plt.plot(points_global) - if (show): + plt.plot(points_global, marker='+', color='red',label='Map Points') + + if show: plt.show() return \ No newline at end of file diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 8daba7f..1a5a066 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -214,6 +214,7 @@ def plot(self, show=False): # Draw as subplots + plt.clf() self.fig.clear() ax1 = self.fig.add_subplot(1, 2, 1) @@ -226,11 +227,11 @@ def plot(self, show=False) ax2 = self.fig.add_subplot(1, 2, 2) - self.plotTraj(seqInd, R, h, save=False, show=False) - # TODO: Plotting for map points self.map.plot(self.fig, show=False) + self.plotTraj(seqInd, R, h, save=False, show=False) + trajSavePath = self.filePaths["trajSave"] trajSavePathInd = os.path.join(trajSavePath, f"{seqInd:04d}.jpg") # plt_savefig_by_axis(trajSavePathInd, self.fig, ax2) From d8f63834f756d2c937a1742d95666bfb4e5b2e6d Mon Sep 17 00:00:00 2001 From: liukeskywalker98 <57304384+liukeskywalker98@users.noreply.github.com> Date: Sun, 1 May 2022 21:23:59 -0400 Subject: [PATCH 19/37] Testing pipeline written. About to start testing. Issues with importing numpy due to upgrade of Python version --- genFakeData.py | 21 +++++++++++---------- motionDistortion.py | 29 ++++++++++++++++++++--------- parseData.py | 12 ++++++------ 3 files changed, 37 insertions(+), 25 deletions(-) diff --git a/genFakeData.py b/genFakeData.py index 7d88997..e718b8c 100644 --- a/genFakeData.py +++ b/genFakeData.py @@ -144,21 +144,22 @@ def generateFakeCorrespondencesPolar(currentFrame=None, theta_deg = np.random.random() * theta_max_deg R = getRotationMatrix(theta_deg, degrees=True) - h = generateTranslationVector(max_translation_m) - transform = np.block([[R, h], - [np.zeros((2,)), 1]]) - T_inv = invert_transform(transform) - R_inv = T_inv[:2, :2] - h_inv = T_inv[:2, 2:] + #h = generateTranslationVector(max_translation_m) + h = np.array([[0], [0]]) + # transform = np.block([[R, h], + # [np.zeros((2,)), 1]]) + # T_inv = invert_transform(transform) + # R_inv = T_inv[:2, :2] + # h_inv = T_inv[:2, 2:] #print(currentFrame.shape) - groundTruth = transformCoords(currentFrame, R_inv, h_inv) + groundTruth = transformCoords(currentFrame, R, h) return groundTruth, currentFrame, theta_deg, h def distort(coords, velocity, frequency, h): coords_norm = coords - h.flatten() # N x 2 - angles = np.arctan2(coords_norm[:, 1], -coords_norm[:, 0]) # - y to follow clockwise convention + angles = np.arctan2(coords_norm[:, 1], -coords_norm[:, 0]) # - x to follow clockwise convention period = 1 / frequency times = angles / (2 * np.pi) * period #print(angles) # lesson: need to use arctan2 wisely, it wraps [-pi, pi] @@ -178,8 +179,8 @@ def distort(coords, velocity, frequency, h): s = np.sin(dtheta) ones = np.ones(times.shape) zeros = np.zeros(times.shape) - distortion = np.array([[c, s, -s* dy - c*dx], - [-s, c, -c*dy + s*dx], + distortion = np.array([[ c, s, -s*dy - c*dx], + [-s, c, -c*dy + s*dx], [zeros, zeros, ones]]) # 3 x 3 x N, need to invert? distorted = np.transpose(distortion, axes = (2, 0, 1)) @ np.expand_dims(coords, axis = 2) # N x 3 x 1 distorted = distorted[:, :2, 0] diff --git a/motionDistortion.py b/motionDistortion.py index 02a5b81..29ece32 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -30,7 +30,7 @@ ''' class MotionDistortionSolver(): - def __init__(self, T_wj0, p_w, p_jt, v_j0, T_wj, lambda_p, lambda_v): + def __init__(self, T_wj0, p_w, p_jt, v_j0, T_wj, sigma_p, sigma_v): # e_p Parameters self.T_wj0 = T_wj0 # Prior transform, T_w,j0 self.T_wj0_inv = np.linalg.inv(T_wj0) @@ -42,13 +42,13 @@ def __init__(self, T_wj0, p_w, p_jt, v_j0, T_wj, lambda_p, lambda_v): self.T_wj_initial = T_wj # Initial Transform guess (T from SVD solution) # Optimization parameters - self.lambda_p = lambda_p # Info matrix, point error, lamdba_p - self.lambda_v = lambda_v # Info matrix, velocity, lambda_v - nv = self.lambda_p.shape[0] - np = self.lambda_v.shape[0] - self.lambda_total = np.block([[lambda_v, np.zeros((nv, np))], - [np.zeros((np, nv)), lambda_p]]) - self.info_sqrt = sp.linalg.sqrtm(np.linalg.inv(self.lambda_total)) # 5 x 5 + self.sigma_p = sigma_p # Info matrix, point error, lamdba_p + self.sigma_v = sigma_v # Info matrix, velocity, sigma_v + nv = self.sigma_p.shape[0] + np = self.sigma_v.shape[0] + self.sigma_total = np.block([[sigma_v, np.zeros((nv, np))], + [np.zeros((np, nv)), sigma_p]]) + self.info_sqrt = sp.linalg.sqrtm(np.linalg.inv(self.sigma_total)) # 5 x 5 self.dT = None self.T_wj_best = T_wj self.v_j_best = v_j0 # might not be good enough a guess, too far from optimal @@ -103,6 +103,11 @@ def expected_observed_pts(self, T_wj): return np.linalg.inv(T_wj) @ self.p_w.T def error_vector(self, params): + ''' + Because we are optimizing over rotations, we choose to keep the rotation + in a theta form, we have to do matrix exponential in here to convert + into the SO(1) form, then augment to the rotation-translation transform + ''' theta = params[2] x = params[0] y = params[1] @@ -118,6 +123,7 @@ def error(self, v_j, T_wj): ''' # Compute point error undistorted = self.undistort(v_j) + #self.compute_time_deltas(undistorted) expected = self.expected_observed_pts(self, T_wj) naive_e_p = expected - np.squeeze(undistorted).T # 3 x N # Actual loss is the Cauchy robust loss, defined here: @@ -125,6 +131,7 @@ def error(self, v_j, T_wj): e_p = np.sum(e_p_i, axis = 1) # 2 x 1 # Compute velocity error + # Matrix log operation T_j_j1 = self.T_wj0_inv @ T_wj dx = T_j_j1[0, 2] dy = T_j_j1[1, 2] @@ -158,7 +165,7 @@ def jacobian(self, v_j, T_wj): input = expected - np.squeeze(undistorted).T # 3 x N cauchy_derivative = input / (np.square(input[:2, ]) / 2 + 1) # 2 x N - # Compute J_p: derivative of e_p wrt + # Compute J_p: derivative of errors wrt the point position c0 = self.T_wj0[0, 0] s0 = self.T_wj0[1, 0] c1 = T_wj[0, 0] @@ -218,6 +225,10 @@ def optimize(self, max_iters = 20): pass def optimize_library(self): + ''' + Optimize using the LM implementation in the scipy library. + ''' + self.compute_time_deltas(self.p_jt) # Initialize v, T T0 = self.T_wj_initial T_params = np.array([T0[0, 2], T0[1, 2], np.arctan2(T0[1, 0], T0[0, 0])]) diff --git a/parseData.py b/parseData.py index 864ffc0..94fa1be 100644 --- a/parseData.py +++ b/parseData.py @@ -1,5 +1,5 @@ from tkinter.messagebox import NO -from typing import Tuple +from typing import Tuple, List import numpy as np import cv2 import os, sys @@ -55,7 +55,7 @@ def extractDataFromRadarImage( def drawCVPoint(img: np.ndarray, point: CartCoord, - point_color: tuple[int, int, int] = (0, 0, 255)): + point_color: Tuple[int, int, int] = (0, 0, 255)): if isinstance(point, CartCoord): point = point.asTuple() @@ -159,7 +159,7 @@ def convertPolarImgToLogPolar(imgPolar: np.ndarray): def getDataFromImgPathsByIndex( - imgPathArr: list[str], index: int + imgPathArr: List[str], index: int ) -> Tuple[np.ndarray, float, float, np.ndarray, np.ndarray, np.ndarray]: ''' @brief Get information from image path array, indexing accordingly @@ -180,7 +180,7 @@ def getDataFromImgPathsByIndex( return extractDataFromRadarImage(imgPolarData) -def getPolarImageFromImgPaths(imgPathArr: list[str], index: int) -> np.ndarray: +def getPolarImageFromImgPaths(imgPathArr: List[str], index: int) -> np.ndarray: ''' @brief Get polar image from image path array, indexing accordingly @param[in] imgPathArr List of image path as strings @@ -193,7 +193,7 @@ def getPolarImageFromImgPaths(imgPathArr: list[str], index: int) -> np.ndarray: return imgPolar -def getCartImageFromImgPaths(imgPathArr: list[str], index: int) -> np.ndarray: +def getCartImageFromImgPaths(imgPathArr: List[str], index: int) -> np.ndarray: ''' @brief Get polar image from image path array, indexing accordingly @param[in] imgPathArr List of image path as strings @@ -206,7 +206,7 @@ def getCartImageFromImgPaths(imgPathArr: list[str], index: int) -> np.ndarray: return convertPolarImageToCartesian(imgPolar) -def getRadarImgPaths(dataPath: str, timestampPath: str) -> list[str]: +def getRadarImgPaths(dataPath: str, timestampPath: str) -> List[str]: ''' @brief Obtain list of radar image paths From a2b18d2fd94dd1472f43bc193d53c99015449e89 Mon Sep 17 00:00:00 2001 From: liukeskywalker98 <57304384+liukeskywalker98@users.noreply.github.com> Date: Sun, 1 May 2022 21:58:05 -0400 Subject: [PATCH 20/37] Adding testing files --- testMotionDistortion.py | 73 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 testMotionDistortion.py diff --git a/testMotionDistortion.py b/testMotionDistortion.py new file mode 100644 index 0000000..ffa3cac --- /dev/null +++ b/testMotionDistortion.py @@ -0,0 +1,73 @@ +from getTransformKLT import * +from genFakeData import * +from motionDistortion import * + +if __name__ == "__main__": + N = 100 + outlier_rate = 0.4 + noisy = False + useOld = False + frequency = 4 + period = 1 / frequency + + # Generate fake data + # srcCoord = frame 1 + # currentFrame = frame 2 + # R_theta_deg @ currentFrame + h = srcCoord + # pose at frame 1 (srcCoord) is I + groundTruth, currentFrame, theta_deg, h = generateFakeCorrespondencesPolar(n_points=N) + # This theta_deg reflects the radar's own motion. To distort points, the opposite must be used + velocity = np.array([h[0, 0], h[1, 0], theta_deg]) / period + distorted = distort(currentFrame, velocity, frequency, h) + if noisy: + currentFrame, outlier_ind = createOutliers(currentFrame, int(N * outlier_rate), + noiseToAdd=10) + + ''' + Naive Fit: rotation and translation + ''' + if useOld: + R_fit, h_fit = calculateTransform(groundTruth, distorted) + + A = np.block([[R_fit, h_fit], + [np.zeros((1, 2)), 1]]) + A_inv = np.linalg.inv(A) + R_fit = A_inv[:2, :2] + h_fit = A_inv[:2, 2:] + else: + R_fit, h_fit = calculateTransformSVD(groundTruth, distorted) + #R_fit, h_fit = calculateTransformDxDth(srcCoord, currentFrame) + + theta_fit = np.arctan2(R_fit[1, 0], R_fit[0, 0]) * 180 / np.pi + print(f"Actual Transform:\ntheta:\n{theta_deg}\nh:\n{h}") + print(f"Fitted Transform:\ntheta:\n{theta_fit}\nh:\n{h_fit}") + + ''' + Applying Motion Distortion Solving + ''' + # Prior Transform + T_wj0 = np.eye(3) + # Point world positions + p_w = groundTruth + # Observed points at time 1 + p_jt = distorted + # Initial velocity guess + v_j0 = np.array([h_fit[0,0], h_fit[1,0], theta_fit]) / period + # Initial Transform guess + T_wj = np.block([[R_fit, h_fit], + [np.zeros((2,)), 1]]) + # Covariance matrix, point errors + cov_p = np.diag([1, 1]) + # Covariance matrix, velocity errors + cov_v = np.diag([16, 16, 8 * np.pi / 180]) # 4 ^2 since 4 Hz + # Information matrix, + MDS = MotionDistortionSolver(T_wj0, p_w, p_jt, v_j0, T_wj, cov_p, cov_v) + params = MDS.optimize_library + print(f"Parameters:\nvx, vy, dx, dy, dtheta\n{np.flatten(params)}") + + # Visualize + srcCoord2 = (R_fit @ distorted.T + h_fit).T + plotFakeFeatures(groundTruth, srcCoord2, + title_append="", alpha=0.5, clear=False, show=True, + plotDisplace = True) + \ No newline at end of file From 3685bd1a5cd91b614c25985cec2ebb910837835a Mon Sep 17 00:00:00 2001 From: Kevin Date: Sun, 1 May 2022 23:07:58 -0400 Subject: [PATCH 21/37] Lots of bug fixes on the way to compilation --- motionDistortion.py | 34 +++++++++++++++++++--------------- testMotionDistortion.py | 2 +- utils.py | 11 ++++++++++- 3 files changed, 30 insertions(+), 17 deletions(-) diff --git a/motionDistortion.py b/motionDistortion.py index 29ece32..573d8a2 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -1,5 +1,6 @@ import numpy as np import scipy as sp +from utils import * #sp.linalg.sqrtm #sp.lin @@ -29,14 +30,16 @@ Debug ''' + + class MotionDistortionSolver(): def __init__(self, T_wj0, p_w, p_jt, v_j0, T_wj, sigma_p, sigma_v): # e_p Parameters self.T_wj0 = T_wj0 # Prior transform, T_w,j0 self.T_wj0_inv = np.linalg.inv(T_wj0) - self.p_w = p_w # Estimated point world positions, N x 3 - self.p_jt = p_jt # Observed points at time t, N x 3 - + self.p_w = homogenize(p_w) # Estimated point world positions, N x 3 + self.p_jt = homogenize(p_jt) # Observed points at time t, N x 3 + # e_v Parameters self.v_j_initial = v_j0 # Initial velocity guess (prior velocity/ velocity from SVD solution) self.T_wj_initial = T_wj # Initial Transform guess (T from SVD solution) @@ -44,10 +47,10 @@ def __init__(self, T_wj0, p_w, p_jt, v_j0, T_wj, sigma_p, sigma_v): # Optimization parameters self.sigma_p = sigma_p # Info matrix, point error, lamdba_p self.sigma_v = sigma_v # Info matrix, velocity, sigma_v - nv = self.sigma_p.shape[0] - np = self.sigma_v.shape[0] - self.sigma_total = np.block([[sigma_v, np.zeros((nv, np))], - [np.zeros((np, nv)), sigma_p]]) + n_v = self.sigma_v.shape[0] + n_p = self.sigma_p.shape[0] + self.sigma_total = np.block([[sigma_v, np.zeros((n_v, n_p))], + [np.zeros((n_p, n_v)), sigma_p]]) self.info_sqrt = sp.linalg.sqrtm(np.linalg.inv(self.sigma_total)) # 5 x 5 self.dT = None self.T_wj_best = T_wj @@ -85,13 +88,15 @@ def undistort(self, v_j): theta = displacement[2, :] dx = displacement[0, :] dy = displacement[1, :] + shape = theta.shape # Correction matrix for time drift, 3 x 3 x N T_j_jt = np.array([[np.cos(theta), -np.sin(theta), dx], [np.sin(theta), np.cos(theta), dy], - [0, 0, 1]]) - - p_jt_col = np.expand_dims(self.p_jt, axis = 1).transpose(axis = (2, 0, 1)) # N x 3 x 1 - undistorted = T_j_jt.transpose(axis = (2, 0, 1)) @ p_jt_col # N x 3 x 1 + [np.zeros(shape), np.zeros(shape), np.ones(shape)]]) + print(T_j_jt.shape) + p_jt_col = np.expand_dims(self.p_jt, axis = 2) # N x 3 x 1 + print(p_jt_col.shape) + undistorted = T_j_jt.transpose((2, 0, 1)) @ p_jt_col # N x 3 x 1 return undistorted @@ -124,11 +129,11 @@ def error(self, v_j, T_wj): # Compute point error undistorted = self.undistort(v_j) #self.compute_time_deltas(undistorted) - expected = self.expected_observed_pts(self, T_wj) + expected = self.expected_observed_pts(T_wj) naive_e_p = expected - np.squeeze(undistorted).T # 3 x N # Actual loss is the Cauchy robust loss, defined here: e_p_i = np.log(np.square(naive_e_p[:2, :]) / 2 + 1) - e_p = np.sum(e_p_i, axis = 1) # 2 x 1 + e_p = np.sum(e_p_i, axis = 1, keepdims = True) # 2 x 1 # Compute velocity error # Matrix log operation @@ -137,8 +142,7 @@ def error(self, v_j, T_wj): dy = T_j_j1[1, 2] dtheta = np.arctan2(T_j_j1[1, 0], T_j_j1[0, 0]) v_j_prior = np.array([dx, dy, dtheta]) / self.total_scan_time - e_v = (v_j - v_j_prior) * e_p.shape[1] # 3 x 1 - + e_v = np.expand_dims((v_j - v_j_prior) * e_p_i.shape[1], axis = 1) # 3 x 1 e = np.vstack((e_v, e_p)) return e diff --git a/testMotionDistortion.py b/testMotionDistortion.py index ffa3cac..3838226 100644 --- a/testMotionDistortion.py +++ b/testMotionDistortion.py @@ -62,7 +62,7 @@ cov_v = np.diag([16, 16, 8 * np.pi / 180]) # 4 ^2 since 4 Hz # Information matrix, MDS = MotionDistortionSolver(T_wj0, p_w, p_jt, v_j0, T_wj, cov_p, cov_v) - params = MDS.optimize_library + params = MDS.optimize_library() print(f"Parameters:\nvx, vy, dx, dy, dtheta\n{np.flatten(params)}") # Visualize diff --git a/utils.py b/utils.py index 8aa02f6..1b1e1f1 100644 --- a/utils.py +++ b/utils.py @@ -153,4 +153,13 @@ def invert_transform(T): T_inv = np.array([[ c, s, -s * y - c * x], [-s, c, -c * y + s * x], [ 0, 0, 1]]) - return T_inv \ No newline at end of file + return T_inv + +def homogenize(points): + result = None + if points.shape[1] == 2: + ones = np.ones((points.shape[0], 1)) + result = np.concatenate((points, ones) , axis = 1) + else: + result = points.copy() + return result \ No newline at end of file From dd755e6d8b059c702bf9f93c384fab7ef7104887 Mon Sep 17 00:00:00 2001 From: Kevin Date: Mon, 2 May 2022 00:31:42 -0400 Subject: [PATCH 22/37] LM in working state --- motionDistortion.py | 34 +++++++++++++++++++++------------- testMotionDistortion.py | 8 ++++---- 2 files changed, 25 insertions(+), 17 deletions(-) diff --git a/motionDistortion.py b/motionDistortion.py index 573d8a2..262a908 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -93,9 +93,7 @@ def undistort(self, v_j): T_j_jt = np.array([[np.cos(theta), -np.sin(theta), dx], [np.sin(theta), np.cos(theta), dy], [np.zeros(shape), np.zeros(shape), np.ones(shape)]]) - print(T_j_jt.shape) p_jt_col = np.expand_dims(self.p_jt, axis = 2) # N x 3 x 1 - print(p_jt_col.shape) undistorted = T_j_jt.transpose((2, 0, 1)) @ p_jt_col # N x 3 x 1 return undistorted @@ -133,7 +131,7 @@ def error(self, v_j, T_wj): naive_e_p = expected - np.squeeze(undistorted).T # 3 x N # Actual loss is the Cauchy robust loss, defined here: e_p_i = np.log(np.square(naive_e_p[:2, :]) / 2 + 1) - e_p = np.sum(e_p_i, axis = 1, keepdims = True) # 2 x 1 + e_p = np.sum(e_p_i, axis = 1) # (2,) # Compute velocity error # Matrix log operation @@ -142,8 +140,9 @@ def error(self, v_j, T_wj): dy = T_j_j1[1, 2] dtheta = np.arctan2(T_j_j1[1, 0], T_j_j1[0, 0]) v_j_prior = np.array([dx, dy, dtheta]) / self.total_scan_time - e_v = np.expand_dims((v_j - v_j_prior) * e_p_i.shape[1], axis = 1) # 3 x 1 - e = np.vstack((e_v, e_p)) + e_v = (v_j - v_j_prior) * e_p_i.shape[1] # (3,) + e = np.hstack((e_p, np.zeros((1,)), e_v)) + #print(e) return e def jacobian_vector(self, params): @@ -165,9 +164,10 @@ def jacobian(self, v_j, T_wj): vx, vy, vtheta ''' undistorted = self.undistort(v_j) - expected = self.expected_observed_pts(self, T_wj) + expected = self.expected_observed_pts(T_wj) input = expected - np.squeeze(undistorted).T # 3 x N - cauchy_derivative = input / (np.square(input[:2, ]) / 2 + 1) # 2 x N + denom = np.vstack((input[:2], np.ones((1, input.shape[1])))) + cauchy_derivative = input / (np.square(denom) / 2 + 1) # 3 x N # Compute J_p: derivative of errors wrt the point position c0 = self.T_wj0[0, 0] @@ -179,11 +179,14 @@ def jacobian(self, v_j, T_wj): pwx = self.p_w[:, 0] # 1 x N pwy = self.p_w[:, 1] ones = np.ones(pwx.shape) + zeros = np.zeros(pwx.shape) # 2 x 3 x N J_p1 = np.array([[-c1 * ones, -s1 * ones, -pwx * s1 + pwy * c1 - c1 * Ty + s1 * Tx], - [s1 * ones, -c1 * ones, -pwx * c1 - pwy * s1 + s1 * Ty + c1 * Tx]]) + [s1 * ones, -c1 * ones, -pwx * c1 - pwy * s1 + s1 * Ty + c1 * Tx], + [zeros, zeros, zeros]]) J_p1 *= np.expand_dims(cauchy_derivative, axis = 1) + J_p1 = np.sum(J_p1, axis = 2) J_p2 = np.array([[ c0, s0, 0], [-s0, c0, 0], [0, 0, 1]]) / self.total_scan_time @@ -195,10 +198,12 @@ def jacobian(self, v_j, T_wj): y = points[:, 1] displacement = np.expand_dims(v_j, axis = 1) * self.dT # 3 x N theta = displacement[2, :] - J_v = np.array([[-self.dT, 0, np.sin(theta) * self.dT * x + np.cos(theta) * self.dT * y ], - [0, -self.dT, -np.cos(theta) * self.dT * x + np.sin(theta) * self.dT * y]]) + zeros = np.zeros(theta.shape) + J_v = np.array([[-self.dT, zeros, np.sin(theta) * self.dT * x + np.cos(theta) * self.dT * y ], + [zeros, -self.dT, -np.cos(theta) * self.dT * x + np.sin(theta) * self.dT * y], + [zeros, zeros, zeros]]) J_v *= np.expand_dims(cauchy_derivative, axis = 1) - J_v = np.sum(J_v, axis = -1) # 3 x 2 + J_v = np.sum(J_v, axis = -1) # 2 x 3 J_v = np.vstack((J_v, np.eye(3) * x.shape[0])) # J = [J_v, J_p] @@ -237,8 +242,10 @@ def optimize_library(self): T0 = self.T_wj_initial T_params = np.array([T0[0, 2], T0[1, 2], np.arctan2(T0[1, 0], T0[0, 0])]) initial_guess = np.hstack((self.v_j_initial, T_params)) + print(f"Initial v guess: {self.v_j_initial}") + print(f"Initial T guess: {T_params}") - result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = self.jacobian_vector, method = 'lm') + result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = '2-point', method = 'lm') # return v, T best_params = result.x @@ -250,7 +257,8 @@ def optimize_library(self): 2 : "ftol termination condition is satisfied", 3 : "xtol termination condition is satisfied", 4 : "Both ftol and xtol termination conditions are satisfied"} - print(f"Final v: {best_params[:3]}, t: {best_params[3:]}") + print(f"Final v: {best_params[:3]}") + print(f"Final t: {best_params[3:]}") print(f"Used {num_evals} evaluations") print(status_dict[status]) return best_params diff --git a/testMotionDistortion.py b/testMotionDistortion.py index 3838226..4c6f620 100644 --- a/testMotionDistortion.py +++ b/testMotionDistortion.py @@ -52,18 +52,18 @@ # Observed points at time 1 p_jt = distorted # Initial velocity guess - v_j0 = np.array([h_fit[0,0], h_fit[1,0], theta_fit]) / period + v_j0 = np.array([h_fit[0,0], h_fit[1,0], theta_fit * np.pi / 180]) / period # Initial Transform guess T_wj = np.block([[R_fit, h_fit], [np.zeros((2,)), 1]]) # Covariance matrix, point errors - cov_p = np.diag([1, 1]) + cov_p = np.diag([8, 8, 8]) # Covariance matrix, velocity errors - cov_v = np.diag([16, 16, 8 * np.pi / 180]) # 4 ^2 since 4 Hz + cov_v = np.diag([4, 4, (2 * np.pi / 180) ** 2]) # 4 ^2 since 4 Hz # Information matrix, MDS = MotionDistortionSolver(T_wj0, p_w, p_jt, v_j0, T_wj, cov_p, cov_v) params = MDS.optimize_library() - print(f"Parameters:\nvx, vy, dx, dy, dtheta\n{np.flatten(params)}") + print(f"Parameters:\nvx, vy, dx, dy, dtheta\n{params.flatten()}") # Visualize srcCoord2 = (R_fit @ distorted.T + h_fit).T From 6be05afe9e788ec65037e06d312501cb1f4075ee Mon Sep 17 00:00:00 2001 From: Kevin Date: Mon, 2 May 2022 02:53:01 -0400 Subject: [PATCH 23/37] Testing code. Fixed a bug in the wrapper for jacobian_vector. Wrote code to visualize undistort --- genFakeData.py | 10 ++-------- motionDistortion.py | 20 +++++++++++--------- testMotionDistortion.py | 26 +++++++++++++++++++++----- 3 files changed, 34 insertions(+), 22 deletions(-) diff --git a/genFakeData.py b/genFakeData.py index e718b8c..99b36b8 100644 --- a/genFakeData.py +++ b/genFakeData.py @@ -144,14 +144,8 @@ def generateFakeCorrespondencesPolar(currentFrame=None, theta_deg = np.random.random() * theta_max_deg R = getRotationMatrix(theta_deg, degrees=True) - #h = generateTranslationVector(max_translation_m) - h = np.array([[0], [0]]) - # transform = np.block([[R, h], - # [np.zeros((2,)), 1]]) - # T_inv = invert_transform(transform) - # R_inv = T_inv[:2, :2] - # h_inv = T_inv[:2, 2:] - #print(currentFrame.shape) + h = generateTranslationVector(max_translation_m) + #h = np.array([[0], [0]]) groundTruth = transformCoords(currentFrame, R, h) return groundTruth, currentFrame, theta_deg, h diff --git a/motionDistortion.py b/motionDistortion.py index 262a908..218fbd2 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -83,11 +83,12 @@ def undistort(self, v_j): Computes a new set of undistorted observed points, based on the current best estimate of v_T, T_wj, dT ''' - displacement = np.expand_dims(v_j, axis = 1) * self.dT # 3 x N - #assert(displacement.shape == (3,points.shape[0])) - theta = displacement[2, :] - dx = displacement[0, :] - dy = displacement[1, :] + v_j_column = np.expand_dims(v_j, axis = 1) + displacement = v_j_column * self.dT # 3 x N + + theta = displacement[2, :] # (N,) + dx = displacement[0, :] # (N,) + dy = displacement[1, :] # (N,) shape = theta.shape # Correction matrix for time drift, 3 x 3 x N T_j_jt = np.array([[np.cos(theta), -np.sin(theta), dx], @@ -146,13 +147,14 @@ def error(self, v_j, T_wj): return e def jacobian_vector(self, params): - theta = params[2] - x = params[0] - y = params[1] + theta = params[5] + x = params[3] + y = params[4] T = np.array([[np.cos(theta), -np.sin(theta), x], [np.sin(theta), np.cos(theta), y], [0 , 0 , 1]]) - return self.info_sqrt @ self.jacobian(params[:3], T) + velocity = params[:3] + return self.info_sqrt @ self.jacobian(velocity, T) def jacobian(self, v_j, T_wj): ''' diff --git a/testMotionDistortion.py b/testMotionDistortion.py index 4c6f620..4336cd8 100644 --- a/testMotionDistortion.py +++ b/testMotionDistortion.py @@ -1,6 +1,7 @@ from getTransformKLT import * from genFakeData import * from motionDistortion import * +import matplotlib.pyplot as plt if __name__ == "__main__": N = 100 @@ -42,6 +43,13 @@ print(f"Actual Transform:\ntheta:\n{theta_deg}\nh:\n{h}") print(f"Fitted Transform:\ntheta:\n{theta_fit}\nh:\n{h_fit}") + # Visualize + plt.subplot(1,2,1) + srcCoord2 = (R_fit @ distorted.T + h_fit).T + plotFakeFeatures(groundTruth, srcCoord2, + title_append="", alpha=0.5, clear=False, show=False, + plotDisplace = True) + ''' Applying Motion Distortion Solving ''' @@ -62,12 +70,20 @@ cov_v = np.diag([4, 4, (2 * np.pi / 180) ** 2]) # 4 ^2 since 4 Hz # Information matrix, MDS = MotionDistortionSolver(T_wj0, p_w, p_jt, v_j0, T_wj, cov_p, cov_v) + MDS.compute_time_deltas(p_jt) + undistorted = MDS.undistort(v_j0) + print(undistorted.shape) + undistorted = undistorted[:, :2, 0] + R_fit, h_fit = calculateTransformSVD(groundTruth, undistorted) + srcCoord3 = (R_fit @ undistorted.T + h_fit).T + plt.subplot(1,2,2) + plotFakeFeatures(groundTruth, srcCoord3, + title_append="", alpha=0.5, clear=False, show=True, + plotDisplace = True) + + params = MDS.optimize_library() print(f"Parameters:\nvx, vy, dx, dy, dtheta\n{params.flatten()}") - # Visualize - srcCoord2 = (R_fit @ distorted.T + h_fit).T - plotFakeFeatures(groundTruth, srcCoord2, - title_append="", alpha=0.5, clear=False, show=True, - plotDisplace = True) + \ No newline at end of file From e21a348d701f9cdbe15f3b25782938769db9491a Mon Sep 17 00:00:00 2001 From: Kevin Date: Mon, 2 May 2022 03:03:19 -0400 Subject: [PATCH 24/37] motionDistort: some debugging comments --- motionDistortion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/motionDistortion.py b/motionDistortion.py index 218fbd2..d9f941f 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -104,7 +104,7 @@ def expected_observed_pts(self, T_wj): Returns the estimated positions of points based on their world location estimates and the current best fit transform ''' - return np.linalg.inv(T_wj) @ self.p_w.T + return T_wj @ self.p_w.T#np.linalg.inv(T_wj) @ self.p_w.T def error_vector(self, params): ''' From 3a2ed4f7acdf94e9255ea2b5ecec92b6a8d453ac Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Mon, 2 May 2022 18:44:42 -0400 Subject: [PATCH 25/37] change dist threshold to 0.5m as that makes more snese --- outlierRejection.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/outlierRejection.py b/outlierRejection.py index 548fb23..e43f7e4 100644 --- a/outlierRejection.py +++ b/outlierRejection.py @@ -7,7 +7,7 @@ from genFakeData import addNoise, createOutliers, generateFakeCorrespondences, plotFakeFeatures # TODO: Tune this -DIST_THRESHOLD_M = 1 # changed from 2.5m +DIST_THRESHOLD_M = 0.5 # changed from 2.5m DIST_THRESHOLD_PX = DIST_THRESHOLD_M / RANGE_RESOLUTION_CART_M # Euclidean distance threshold # NOTE: this is Euclid distance squared (i.e. 25 = ~5 px of error allowed) DISTSQ_THRESHOLD_PX = DIST_THRESHOLD_PX * DIST_THRESHOLD_PX From de832303f32ad012ee87bb1fb199b4aebedd47b8 Mon Sep 17 00:00:00 2001 From: Samuel Leong Date: Mon, 2 May 2022 18:50:27 -0400 Subject: [PATCH 26/37] Fix issue with plotting map points - need to translate then rotate --- Mapping.py | 8 +++++--- img/roam_mapping/tiny_traj.mp4 | Bin 0 -> 74565 bytes img/roam_mapping/tiny_traj/0001.jpg | Bin 0 -> 80644 bytes img/roam_mapping/tiny_traj/0002.jpg | Bin 0 -> 72596 bytes img/roam_mapping/tiny_traj/0003.jpg | Bin 0 -> 72652 bytes img/roam_mapping/tiny_traj/0004.jpg | Bin 0 -> 70961 bytes img/roam_mapping/tiny_traj/0005.jpg | Bin 0 -> 71534 bytes img/roam_mapping/tiny_traj/0006.jpg | Bin 0 -> 71384 bytes img/roam_mapping/tiny_traj/0007.jpg | Bin 0 -> 72580 bytes img/roam_mapping/tiny_traj/0008.jpg | Bin 0 -> 73682 bytes img/roam_mapping/tiny_traj/0009.jpg | Bin 0 -> 77923 bytes img/roam_mapping/tiny_traj/0010.jpg | Bin 0 -> 75450 bytes 12 files changed, 5 insertions(+), 3 deletions(-) create mode 100644 img/roam_mapping/tiny_traj.mp4 create mode 100644 img/roam_mapping/tiny_traj/0001.jpg create mode 100644 img/roam_mapping/tiny_traj/0002.jpg create mode 100644 img/roam_mapping/tiny_traj/0003.jpg create mode 100644 img/roam_mapping/tiny_traj/0004.jpg create mode 100644 img/roam_mapping/tiny_traj/0005.jpg create mode 100644 img/roam_mapping/tiny_traj/0006.jpg create mode 100644 img/roam_mapping/tiny_traj/0007.jpg create mode 100644 img/roam_mapping/tiny_traj/0008.jpg create mode 100644 img/roam_mapping/tiny_traj/0009.jpg create mode 100644 img/roam_mapping/tiny_traj/0010.jpg diff --git a/Mapping.py b/Mapping.py index 59409dc..9dcbbff 100644 --- a/Mapping.py +++ b/Mapping.py @@ -79,10 +79,12 @@ def convertFeaturesLocalToGlobal( # Then we need to convert to meters featurePointsGlobal *= RANGE_RESOLUTION_CART_M # px * (m/px) = m + # Center origin at pose center + # Rotate and translate to make into global coordinate system R = getRotationMatrix(th) t = np.array([x, y]).reshape(2, 1) - featurePointsGlobal = (R @ featurePointsGlobal.T + t).T + featurePointsGlobal = (R @ (featurePointsGlobal.T + t)).T return featurePointsGlobal @@ -198,8 +200,8 @@ def plot(self, fig: plt.figure, show: bool = False): for kf in self.keyframes: points_local = kf.featurePointsLocal points_global = kf.convertFeaturesLocalToGlobal(points_local) - - plt.plot(points_global, marker='+', color='red',label='Map Points') + + plt.scatter(points_global[:, 0], points_global[:, 1], marker='+', color='red',label='Map Points') if show: plt.show() diff --git a/img/roam_mapping/tiny_traj.mp4 b/img/roam_mapping/tiny_traj.mp4 new file mode 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z_N)}t`*_cw$MO!H{E9l+o}L|HdNI9^2ETc~f^o`Ch>w;-Fa=xi(Wa!BlqD zzFWAD``7LciW4 zTTOMEY9(6dkn(Y^%#N%t3`^d2A_+Ieq7dvQ~dB@S}JrB2#5Nz7s zO^&{adp&fF&04R<-v=h%yJ1_GI#>q_Rd&Y%_aWlxwii~AGg)8G`i1kv5IUv_aWtT1Un;uKqDTz#~=$v-Iry6Yb zt~nn2M-vd*JZW0=zDl=Q2IS;`h~e^DEiocmsi3?$K6FBd=p!bjMFf`*>#*hKiHrb? z3t(^;6wu!g0>J_+xr>BLEmQx|9vk9{Mtvw*Pl5EgFRotu==sQ^bI{S;{S zDmXSW_V&{XEZ983|4!HML-6Umct%P69nvoMniqpEA@n-*@cY63O ziS9oFO8@w(nRJ}qs9hT|*Y1fktm3Vvb(<>jnW{s7l@%xH>-tmr?fKS)*8t)606$jN z)QXB&ftMc@ey{`*sL0TUy8zt!S+*g(q_SHZHp!Kx5p?wL#8*ZHW_>Y3tuR|=@)}?d z-ZlC4GZ2e{wmQ{^wn{VxJy=sMVtq%bYqh)^a-M9KP-F3J-9%4nNR(I=C-+th0r#5@ z()Uvjuo^t~MsK$)VvOPySd0)vU(%P|61D1%*4gDP4RX!uhTrjt%)u5-epHZA70~^n zU9edLg$vdfo4An;OL3sjb%p_qSDcAaQJS3|OwB70`+t|GLi>O5GZbAsaqm{Ynoa-} z`%z-Alio=5U5+ngi?#!hW?Y(x*rBg)xDGEU&#bH zw_tS$YEPT6OjK%4eVI#>h~W}yL#lkgV67abg5p1j`sbtuH^nGIFvud#Jv*tSBhl!* zZyUEtu=V5<8IHiJ#_+RhC~1P5RV&GuFhlXX$;VX9ODGclsK${*!-B1@VXE Tn}0wV@&CW~kH-&qIr`rKyKuA) literal 0 HcmV?d00001 From fe5fd5363ba108ad3ba34ab03de9e43cb5cdb80b Mon Sep 17 00:00:00 2001 From: Kevin Date: Mon, 2 May 2022 22:18:56 -0400 Subject: [PATCH 27/37] Found and fixed bug in the motion distort Jacobian --- genFakeData.py | 1 - motionDistortion.py | 9 +++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/genFakeData.py b/genFakeData.py index 99b36b8..0f681c3 100644 --- a/genFakeData.py +++ b/genFakeData.py @@ -166,7 +166,6 @@ def distort(coords, velocity, frequency, h): #print(displacement) #print(displacement) dx = displacement[0, :] - print(dx) dy = displacement[1, :] dtheta = displacement[2, :] / 180 * np.pi c = np.cos(dtheta) diff --git a/motionDistortion.py b/motionDistortion.py index d9f941f..c439a57 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -98,13 +98,13 @@ def undistort(self, v_j): undistorted = T_j_jt.transpose((2, 0, 1)) @ p_jt_col # N x 3 x 1 return undistorted - + # TODO: Check if this really should be inverted def expected_observed_pts(self, T_wj): ''' Returns the estimated positions of points based on their world location estimates and the current best fit transform ''' - return T_wj @ self.p_w.T#np.linalg.inv(T_wj) @ self.p_w.T + return np.linalg.inv(T_wj) @ self.p_w.T def error_vector(self, params): ''' @@ -191,7 +191,7 @@ def jacobian(self, v_j, T_wj): J_p1 = np.sum(J_p1, axis = 2) J_p2 = np.array([[ c0, s0, 0], [-s0, c0, 0], - [0, 0, 1]]) / self.total_scan_time + [0, 0, 1]]) / self.total_scan_time * pwx.shape[0] J_p = np.vstack((J_p1, J_p2)) # Compute J_v: derivative of the errors wrt velocity @@ -205,7 +205,7 @@ def jacobian(self, v_j, T_wj): [zeros, -self.dT, -np.cos(theta) * self.dT * x + np.sin(theta) * self.dT * y], [zeros, zeros, zeros]]) J_v *= np.expand_dims(cauchy_derivative, axis = 1) - J_v = np.sum(J_v, axis = -1) # 2 x 3 + J_v = np.sum(J_v, axis = -1) # 3 x 3 J_v = np.vstack((J_v, np.eye(3) * x.shape[0])) # J = [J_v, J_p] @@ -262,5 +262,6 @@ def optimize_library(self): print(f"Final v: {best_params[:3]}") print(f"Final t: {best_params[3:]}") print(f"Used {num_evals} evaluations") + print(f"Residuals were {result.fun}") print(status_dict[status]) return best_params From a02ee2a048fa8d8065080de42bbdcba4fb9ee771 Mon Sep 17 00:00:00 2001 From: Kevin Date: Tue, 3 May 2022 00:37:25 -0400 Subject: [PATCH 28/37] Fixed bug in error vector formatting --- motionDistortion.py | 17 ++++++++++++----- testMotionDistortion.py | 4 ++-- 2 files changed, 14 insertions(+), 7 deletions(-) diff --git a/motionDistortion.py b/motionDistortion.py index c439a57..24c6ee2 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -52,6 +52,10 @@ def __init__(self, T_wj0, p_w, p_jt, v_j0, T_wj, sigma_p, sigma_v): self.sigma_total = np.block([[sigma_v, np.zeros((n_v, n_p))], [np.zeros((n_p, n_v)), sigma_p]]) self.info_sqrt = sp.linalg.sqrtm(np.linalg.inv(self.sigma_total)) # 5 x 5 + sigma_p_vector = np.tile(np.diag(sigma_p), p_jt.shape[0]) + sigma_v_vector = np.diag(sigma_v) + sigma_vector = np.concatenate((sigma_p_vector, sigma_v_vector)) + self.info_vector = 1 / sigma_vector self.dT = None self.T_wj_best = T_wj self.v_j_best = v_j0 # might not be good enough a guess, too far from optimal @@ -118,7 +122,8 @@ def error_vector(self, params): T = np.array([[np.cos(theta), -np.sin(theta), x], [np.sin(theta), np.cos(theta), y], [0 , 0 , 1]]) - return self.info_sqrt @ self.error(params[:3], T) + #return self.info_sqrt @ self.error(params[:3], T) + return self.info_vector * self.error(params[:3], T) def error(self, v_j, T_wj): ''' @@ -131,8 +136,9 @@ def error(self, v_j, T_wj): expected = self.expected_observed_pts(T_wj) naive_e_p = expected - np.squeeze(undistorted).T # 3 x N # Actual loss is the Cauchy robust loss, defined here: - e_p_i = np.log(np.square(naive_e_p[:2, :]) / 2 + 1) - e_p = np.sum(e_p_i, axis = 1) # (2,) + e_p_i = np.log(np.square(naive_e_p[:2, :]) / 2 + 1) # 2 x N + e_p = e_p_i.flatten(order='F') + #e_p = np.sum(e_p_i, axis = 1) # (2,) # Compute velocity error # Matrix log operation @@ -141,8 +147,9 @@ def error(self, v_j, T_wj): dy = T_j_j1[1, 2] dtheta = np.arctan2(T_j_j1[1, 0], T_j_j1[0, 0]) v_j_prior = np.array([dx, dy, dtheta]) / self.total_scan_time + print(f"Prior velocity: {v_j_prior}") e_v = (v_j - v_j_prior) * e_p_i.shape[1] # (3,) - e = np.hstack((e_p, np.zeros((1,)), e_v)) + e = np.hstack((e_p, e_v)) #print(e) return e @@ -155,7 +162,7 @@ def jacobian_vector(self, params): [0 , 0 , 1]]) velocity = params[:3] return self.info_sqrt @ self.jacobian(velocity, T) - + def jacobian(self, v_j, T_wj): ''' Compute the Jacobian. This has two parts, as defined by the RadarSLAM diff --git a/testMotionDistortion.py b/testMotionDistortion.py index 4336cd8..e06395c 100644 --- a/testMotionDistortion.py +++ b/testMotionDistortion.py @@ -65,9 +65,9 @@ T_wj = np.block([[R_fit, h_fit], [np.zeros((2,)), 1]]) # Covariance matrix, point errors - cov_p = np.diag([8, 8, 8]) + cov_p = np.diag([4, 4]) # Covariance matrix, velocity errors - cov_v = np.diag([4, 4, (2 * np.pi / 180) ** 2]) # 4 ^2 since 4 Hz + cov_v = np.diag([1, 1, (5 * np.pi / 180) ** 2]) # 4 ^2 since 4 Hz # Information matrix, MDS = MotionDistortionSolver(T_wj0, p_w, p_jt, v_j0, T_wj, cov_p, cov_v) MDS.compute_time_deltas(p_jt) From 59b5dc28907df5ae6ff47dec29103207e0cabad1 Mon Sep 17 00:00:00 2001 From: Kevin Date: Tue, 3 May 2022 00:51:25 -0400 Subject: [PATCH 29/37] Finally fixed it --- motionDistortion.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/motionDistortion.py b/motionDistortion.py index 24c6ee2..85def52 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -116,9 +116,9 @@ def error_vector(self, params): in a theta form, we have to do matrix exponential in here to convert into the SO(1) form, then augment to the rotation-translation transform ''' - theta = params[2] - x = params[0] - y = params[1] + theta = params[5] + x = params[4] + y = params[3] T = np.array([[np.cos(theta), -np.sin(theta), x], [np.sin(theta), np.cos(theta), y], [0 , 0 , 1]]) @@ -147,7 +147,7 @@ def error(self, v_j, T_wj): dy = T_j_j1[1, 2] dtheta = np.arctan2(T_j_j1[1, 0], T_j_j1[0, 0]) v_j_prior = np.array([dx, dy, dtheta]) / self.total_scan_time - print(f"Prior velocity: {v_j_prior}") + #print(f"Prior velocity: {v_j_prior}") e_v = (v_j - v_j_prior) * e_p_i.shape[1] # (3,) e = np.hstack((e_p, e_v)) #print(e) From bd1a837ac62ebe77f24812c10cabeecd6f3e7a7d Mon Sep 17 00:00:00 2001 From: Kevin Date: Tue, 3 May 2022 02:55:40 -0400 Subject: [PATCH 30/37] Jacobians still do not work. Too much error for velocity --- motionDistortion.py | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/motionDistortion.py b/motionDistortion.py index 85def52..ab1424f 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -148,7 +148,10 @@ def error(self, v_j, T_wj): dtheta = np.arctan2(T_j_j1[1, 0], T_j_j1[0, 0]) v_j_prior = np.array([dx, dy, dtheta]) / self.total_scan_time #print(f"Prior velocity: {v_j_prior}") - e_v = (v_j - v_j_prior) * e_p_i.shape[1] # (3,) + v_diff = (v_j - v_j_prior) + v_diff[2] = normalize_angles(v_diff[2]) + e_v = v_diff * e_p_i.shape[1] # (3,) + # ideally should warp2pi here on theta error e = np.hstack((e_p, e_v)) #print(e) return e @@ -161,7 +164,8 @@ def jacobian_vector(self, params): [np.sin(theta), np.cos(theta), y], [0 , 0 , 1]]) velocity = params[:3] - return self.info_sqrt @ self.jacobian(velocity, T) + #return self.info_sqrt @ self.jacobian(velocity, T) + return np.expand_dims(self.info_vector, axis=1) * self.jacobian(velocity, T) def jacobian(self, v_j, T_wj): ''' @@ -175,8 +179,8 @@ def jacobian(self, v_j, T_wj): undistorted = self.undistort(v_j) expected = self.expected_observed_pts(T_wj) input = expected - np.squeeze(undistorted).T # 3 x N - denom = np.vstack((input[:2], np.ones((1, input.shape[1])))) - cauchy_derivative = input / (np.square(denom) / 2 + 1) # 3 x N + naive_e_p = input[:2] + cauchy_derivative = naive_e_p / (np.square(naive_e_p) / 2 + 1) # 3 x N # Compute J_p: derivative of errors wrt the point position c0 = self.T_wj0[0, 0] @@ -192,10 +196,9 @@ def jacobian(self, v_j, T_wj): # 2 x 3 x N J_p1 = np.array([[-c1 * ones, -s1 * ones, -pwx * s1 + pwy * c1 - c1 * Ty + s1 * Tx], - [s1 * ones, -c1 * ones, -pwx * c1 - pwy * s1 + s1 * Ty + c1 * Tx], - [zeros, zeros, zeros]]) + [s1 * ones, -c1 * ones, -pwx * c1 - pwy * s1 + s1 * Ty + c1 * Tx]]) J_p1 *= np.expand_dims(cauchy_derivative, axis = 1) - J_p1 = np.sum(J_p1, axis = 2) + J_p1 = np.squeeze(np.vstack(np.split(J_p1, J_p1.shape[2], axis = 2))) J_p2 = np.array([[ c0, s0, 0], [-s0, c0, 0], [0, 0, 1]]) / self.total_scan_time * pwx.shape[0] @@ -208,11 +211,10 @@ def jacobian(self, v_j, T_wj): displacement = np.expand_dims(v_j, axis = 1) * self.dT # 3 x N theta = displacement[2, :] zeros = np.zeros(theta.shape) - J_v = np.array([[-self.dT, zeros, np.sin(theta) * self.dT * x + np.cos(theta) * self.dT * y ], - [zeros, -self.dT, -np.cos(theta) * self.dT * x + np.sin(theta) * self.dT * y], - [zeros, zeros, zeros]]) + J_v = np.array([[-self.dT, zeros, self.dT * (np.sin(theta) * x + np.cos(theta) * y) ], + [zeros, -self.dT, self.dT * (-np.cos(theta) * x + np.sin(theta) * y)]]) J_v *= np.expand_dims(cauchy_derivative, axis = 1) - J_v = np.sum(J_v, axis = -1) # 3 x 3 + J_v = np.squeeze(np.vstack(np.split(J_v, J_v.shape[2], axis = 2))) # 2N x 3 J_v = np.vstack((J_v, np.eye(3) * x.shape[0])) # J = [J_v, J_p] @@ -254,8 +256,8 @@ def optimize_library(self): print(f"Initial v guess: {self.v_j_initial}") print(f"Initial T guess: {T_params}") - result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = '2-point', method = 'lm') - + #result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = '2-point', method = 'lm') + result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = self.jacobian_vector, method = 'lm') # return v, T best_params = result.x num_evals = result.nfev # number of function evaluations: measure of how quickly we converged From 58760c7e4e0717ccbca19d3004b4f8b5e09126f3 Mon Sep 17 00:00:00 2001 From: Kevin Date: Tue, 3 May 2022 03:22:58 -0400 Subject: [PATCH 31/37] Added final code to test file to visualize application of undistort --- motionDistortion.py | 8 ++++---- testMotionDistortion.py | 7 +++++++ 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/motionDistortion.py b/motionDistortion.py index ab1424f..47e0b07 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -117,8 +117,8 @@ def error_vector(self, params): into the SO(1) form, then augment to the rotation-translation transform ''' theta = params[5] - x = params[4] - y = params[3] + x = params[3] + y = params[4] T = np.array([[np.cos(theta), -np.sin(theta), x], [np.sin(theta), np.cos(theta), y], [0 , 0 , 1]]) @@ -256,8 +256,8 @@ def optimize_library(self): print(f"Initial v guess: {self.v_j_initial}") print(f"Initial T guess: {T_params}") - #result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = '2-point', method = 'lm') - result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = self.jacobian_vector, method = 'lm') + result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = '2-point', method = 'lm') + # result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = self.jacobian_vector, method = 'lm') # return v, T best_params = result.x num_evals = result.nfev # number of function evaluations: measure of how quickly we converged diff --git a/testMotionDistortion.py b/testMotionDistortion.py index e06395c..c36a5c8 100644 --- a/testMotionDistortion.py +++ b/testMotionDistortion.py @@ -84,6 +84,13 @@ params = MDS.optimize_library() print(f"Parameters:\nvx, vy, dx, dy, dtheta\n{params.flatten()}") + final_undistorted = MDS.undistort(params[:3]) + transform = convertPoseToTransform(params[3:]) + solution = (transform @ final_undistorted)[:, :2, 0] + plt.figure() + plotFakeFeatures(groundTruth, solution, + title_append="", alpha=0.5, clear=False, show=True, + plotDisplace = True) \ No newline at end of file From 69f17fd722a529b13ad55b89ffd63deebf30c5f7 Mon Sep 17 00:00:00 2001 From: Kevin Date: Tue, 3 May 2022 03:44:15 -0400 Subject: [PATCH 32/37] Added comments guiding next steps in testMotionDistortion.py --- testMotionDistortion.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/testMotionDistortion.py b/testMotionDistortion.py index c36a5c8..5840d2d 100644 --- a/testMotionDistortion.py +++ b/testMotionDistortion.py @@ -53,7 +53,7 @@ ''' Applying Motion Distortion Solving ''' - # Prior Transform + # Prior frame's pose T_wj0 = np.eye(3) # Point world positions p_w = groundTruth @@ -92,5 +92,9 @@ title_append="", alpha=0.5, clear=False, show=True, plotDisplace = True) - + #TODO: In main loop: for each iteration, track the previous frame's points. + # If we need to generate new points, create a new keyframe and add its points + # to the global map, using the undistort's velocity and transform to determine + # true position. We need to be able to associate local points to global points + # in each keyframe, and associate global points to those \ No newline at end of file From 1a87b8a70f473c1ac3bbf552a541541d672827c7 Mon Sep 17 00:00:00 2001 From: liukeskywalker98 <57304384+liukeskywalker98@users.noreply.github.com> Date: Tue, 3 May 2022 21:16:09 -0400 Subject: [PATCH 33/37] Preliminary mapping and undistort code --- Mapping.py | 16 ++++++-- RawROAMSystem.py | 60 +++++++++++++++++++++------ Tracker.py | 6 ++- motionDistortion.py | 95 ++++++++++++++++++++++++++++++------------- trajectoryPlotting.py | 11 +++++ 5 files changed, 141 insertions(+), 47 deletions(-) diff --git a/Mapping.py b/Mapping.py index 9dcbbff..63066c2 100644 --- a/Mapping.py +++ b/Mapping.py @@ -7,6 +7,7 @@ # import m2dp from getPointCloud import getPointCloudPolarInd from utils import getRotationMatrix +from motionDistortion import MotionDistortionSolver # Thresholds ROT_THRESHOLD = 0.2 # radians @@ -20,7 +21,7 @@ class Keyframe(): def __init__(self, globalPose: np.ndarray, featurePointsLocal: np.ndarray, - radarPolarImg: np.ndarray) -> None: + radarPolarImg: np.ndarray, velocity: np.ndarray) -> None: ''' @brief Keyframe class. Contains pose, feature points and point cloud information @param[in] globalPose (3 x 1) Pose information [x, y, th] in global coordinates, @@ -31,11 +32,12 @@ def __init__(self, globalPose: np.ndarray, featurePointsLocal: np.ndarray, @see updateInfo() ''' - self.updateInfo(globalPose, featurePointsLocal, radarPolarImg) + self.updateInfo(globalPose, featurePointsLocal, radarPolarImg, velocity) def updateInfo(self, globalPose: np.ndarray, featurePointsLocal: np.ndarray, - radarPolarImg: np.ndarray) -> None: + radarPolarImg: np.ndarray, + velocity: np.ndarray) -> None: ''' @brief Update internal information: pose, feature points and point cloud information @param[in] globalPose (3 x 1) Pose information [x, y, th] in global coordinates, @@ -59,6 +61,10 @@ def updateInfo(self, globalPose: np.ndarray, # TODO: Not sure if needed/useful self.pointCloud = getPointCloudPolarInd(radarPolarImg) + self.velocity = velocity + self.featurePointsLocalUndistorted = MotionDistortionSolver.undistort(velocity, featurePointsLocal) + self.prunedUndistortedLocals = self.featurePointsLocalUndistorted + def copyFromOtherKeyframe(self, keyframe) -> None: self.updateInfo(keyframe.pose, keyframe.featurePointsLocal, keyframe.radarPolarImg) @@ -87,6 +93,9 @@ def convertFeaturesLocalToGlobal( featurePointsGlobal = (R @ (featurePointsGlobal.T + t)).T return featurePointsGlobal + + def getPrunedFeaturesGlobalPosition(self): + return self.convertFeaturesLocalToGlobal(self.prunedUndistortedLocals) def pruneFeaturePoints(self, corrStatus: np.ndarray) -> None: ''' @@ -95,6 +104,7 @@ def pruneFeaturePoints(self, corrStatus: np.ndarray) -> None: @note In place changing of `self.prunedFeaturePoints` function, which aims to track and prune away the feature points that are part of this keyframe ''' self.prunedFeaturePoints = self.prunedFeaturePoints[corrStatus] + self.prunedUndistortedLocals = self.prunedUndistortedLocals[corrStatus] # def isVisible(self, keyframe): # ''' diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 1a5a066..0e5a02d 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -9,7 +9,8 @@ from trajectoryPlotting import Trajectory, getGroundTruthTrajectory, plotGtAndEstTrajectory from utils import convertRandHtoDeltas, f_arr, getRotationMatrix, plt_savefig_by_axis, radarImgPathToTimestamp from Tracker import Tracker - +from motionDistortion import MotionDistortionSolver +from utils import * class RawROAMSystem(): @@ -124,6 +125,15 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: self.gtTraj = gtTraj self.estTraj = estTraj + # Initialialize Motion Distortion Solver + # Covariance matrix, point errors + cov_p = np.diag([4, 4]) # sigma = 2 pixels + # Covariance matrix, velocity errors + cov_v = np.diag([1, 1, (5 * np.pi / 180) ** 2]) # 1 pixel/s, 1 pixel/s, 5 degrees/s + MDS = MotionDistortionSolver(cov_p, cov_v) + # Prior frame's pose + prev_pose = convertPoseToTransform(initPose) + # Actually run the algorithm # Get initial polar and Cartesian image prevImgPolar = getPolarImageFromImgPaths(imgPathArr, startSeqInd) @@ -134,7 +144,8 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: blobCoord, _ = appendNewFeatures(prevImgCart, blobCoord) # Initialize first keyframe - old_kf = Keyframe(initPose, blobCoord, prevImgPolar) + zero_velocity = np.zeros((3,)) + old_kf = Keyframe(initPose, blobCoord, prevImgPolar, ) # pointer to previous kf self.map.addKeyframe(old_kf) possible_kf = Keyframe(initPose, blobCoord, prevImgPolar) @@ -149,21 +160,31 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: good_old, good_new, rotAngleRad, corrStatus = tracker.track( prevImgCart, currImgCart, prevImgPolar, currImgPolar, blobCoord, seqInd) - + + # Keyframe updating + old_kf.pruneFeaturePoints(corrStatus) + print("Detected", np.rad2deg(rotAngleRad), "[deg] rotation") estR = getRotationMatrix(-rotAngleRad) - - R, h = tracker.getTransform(good_old, good_new) - + + R, h = tracker.getTransform(good_old, good_new, pixel = False) # R = estR + + # Solve for Motion Compensated Transform + p_w = old_kf.getPrunedFeaturesGlobalPosition() + # Initial Transform guess + T_wj = np.block([[R, h], + [np.zeros((2,)), 1]]) - # Update trajectory - self.updateTrajectory(R, h, seqInd) + MDS.update_problem(prev_pose, p_w, good_new, T_wj) + undistort_solution = MDS.optimize_library() + pose_vector = undistort_solution[3:] - # Keyframe updating - old_kf.pruneFeaturePoints(corrStatus) + # Update trajectory + #self.updateTrajectory(R, h, seqInd) + self.updateTrajectoryAbsolute(pose_vector, seqInd) - latestPose = self.estTraj.poses[-1] + latestPose = pose_vector #self.estTraj.poses[-1] possible_kf.updateInfo(latestPose, good_new, currImgPolar) # Add a keyframe if it fulfills criteria @@ -179,9 +200,15 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: print("\nAdding keyframe...\n") - old_kf.copyFromOtherKeyframe(possible_kf) - self.map.addKeyframe(old_kf) + #old_kf.copyFromOtherKeyframe(possible_kf) + self.map.addKeyframe(possible_kf) + # TODO: Aliasing above? old_kf is never assigned the object possible_kf, + # map ends up with a list of N pointers to the same keyframe + # Proposed fix: old_kf = possible_kf # switch ptr to new object + # Initialize new poss_kf for new ptr + old_kf = possible_kf + possible_kf = Keyframe(latestPose, good_new, currImgPolar) # TODO: do bundle adjustment here self.map.bundleAdjustment() @@ -192,6 +219,7 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # Update incremental variables blobCoord = good_new.copy() prevImgCart = currImgCart + prev_pose = convertPoseToTransform(latestPose) # TODO: Move into trajectory class? def updateTrajectory(self, R, h, seqInd): @@ -202,6 +230,12 @@ def updateTrajectory(self, R, h, seqInd): self.estTraj.appendRelativeDeltas(timestamp, est_deltas) # self.estTraj.appendRelativeTransform(timestamp, R, h) + def updateTrajectoryAbsolute(self, pose_vector, seqInd): + imgPathArr = self.imgPathArr + + timestamp = radarImgPathToTimestamp(imgPathArr[seqInd]) + self.estTraj.appendAbsoluteTransform(timestamp, pose_vector) + def plot(self, prevImg, currImg, diff --git a/Tracker.py b/Tracker.py index 52cb7f6..73450b0 100644 --- a/Tracker.py +++ b/Tracker.py @@ -95,7 +95,8 @@ def track( return good_old, good_new, angleRotRad, corrStatus def getTransform(self, srcCoord: np.ndarray, - targetCoord: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + targetCoord: np.ndarray, + pixel: bool) -> Tuple[np.ndarray, np.ndarray]: ''' @brief Obtain transform from coordinate correspondnces @@ -109,7 +110,8 @@ def getTransform(self, srcCoord: np.ndarray, # Obtain transforms # R, h = calculateTransformDxDth(srcCoord, targetCoord) R, h = calculateTransformSVD(srcCoord, targetCoord) - h *= RANGE_RESOLUTION_CART_M + if not pixel: + h *= RANGE_RESOLUTION_CART_M return R, h diff --git a/motionDistortion.py b/motionDistortion.py index 47e0b07..0515c84 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -30,41 +30,76 @@ Debug ''' - +RADAR_SCAN_FREQUENCY = 4 # 4 hz data class MotionDistortionSolver(): - def __init__(self, T_wj0, p_w, p_jt, v_j0, T_wj, sigma_p, sigma_v): + def __init__(self, T_wj0, p_w, p_jt, T_wj, sigma_p, sigma_v, + frequency = RADAR_SCAN_FREQUENCY): # e_p Parameters self.T_wj0 = T_wj0 # Prior transform, T_w,j0 self.T_wj0_inv = np.linalg.inv(T_wj0) self.p_w = homogenize(p_w) # Estimated point world positions, N x 3 self.p_jt = homogenize(p_jt) # Observed points at time t, N x 3 - + self.T_wj_initial = T_wj + + # Radar Data Params + self.total_scan_time = 1 / frequency + # e_v Parameters - self.v_j_initial = v_j0 # Initial velocity guess (prior velocity/ velocity from SVD solution) - self.T_wj_initial = T_wj # Initial Transform guess (T from SVD solution) + self.v_j_initial = self.infer_velocity(T_wj) + # Initial velocity guess (prior velocity/ velocity from SVD solution) # Optimization parameters self.sigma_p = sigma_p # Info matrix, point error, lamdba_p self.sigma_v = sigma_v # Info matrix, velocity, sigma_v - n_v = self.sigma_v.shape[0] - n_p = self.sigma_p.shape[0] - self.sigma_total = np.block([[sigma_v, np.zeros((n_v, n_p))], - [np.zeros((n_p, n_v)), sigma_p]]) - self.info_sqrt = sp.linalg.sqrtm(np.linalg.inv(self.sigma_total)) # 5 x 5 + # n_v = self.sigma_v.shape[0] + # n_p = self.sigma_p.shape[0] + # self.sigma_total = np.block([[sigma_v, np.zeros((n_v, n_p))], + # [np.zeros((n_p, n_v)), sigma_p]]) + # self.info_sqrt = sp.linalg.sqrtm(np.linalg.inv(self.sigma_total)) sigma_p_vector = np.tile(np.diag(sigma_p), p_jt.shape[0]) sigma_v_vector = np.diag(sigma_v) sigma_vector = np.concatenate((sigma_p_vector, sigma_v_vector)) self.info_vector = 1 / sigma_vector - self.dT = None - self.T_wj_best = T_wj - self.v_j_best = v_j0 # might not be good enough a guess, too far from optimal + self.dT = MotionDistortionSolver.compute_time_deltas(self.total_scan_time, p_jt) + pass + def __init__(self, sigma_p, sigma_v, + frequency = RADAR_SCAN_FREQUENCY): # Radar Data Params - self.total_scan_time = 1 / 4 # assuming 4 Hz + self.total_scan_time = 1 / frequency + + # Optimization parameters + self.sigma_p = np.diag(sigma_p) # Info matrix, point error, lamdba_p + self.sigma_v = np.diag(sigma_v) # Info matrix, velocity, sigma_v pass + + def update_problem(self, T_wj0, p_w, p_jt, T_wj): + # e_p Parameters + self.T_wj0 = T_wj0 # Prior transform, T_w,j0 + self.T_wj0_inv = np.linalg.inv(T_wj0) + self.p_w = homogenize(p_w) # Estimated point world positions, N x 3 + self.p_jt = homogenize(p_jt) # Observed points at time t, N x 3 + + # e_v Parameters + self.v_j_initial = self.infer_velocity(T_wj) + # Initial velocity guess (prior velocity/ velocity from SVD solution) + self.dT = MotionDistortionSolver.compute_time_deltas(self.total_scan_time, p_jt) + + # Info matrix scales to number of points in the optimization problem + sigma_p_vector = np.tile(self.sigma_p, p_jt.shape[0]) + sigma_v_vector = self.sigma_v + sigma_vector = np.concatenate((sigma_p_vector, sigma_v_vector)) + self.info_vector = 1 / sigma_vector + + def infer_velocity(self, transform): + dx = transform[0, 2] + dy = transform[1, 2] + dtheta = np.arctan2(transform[1, 0], transform[0, 0]) + return np.array([dx, dy, dtheta]) / self.total_scan_time - def compute_time_deltas(self, points): + @staticmethod + def compute_time_deltas(period, points): ''' Get the time deltas for each point. This depends solely on where the points are in scan angle. The further away from center, the greater the @@ -74,21 +109,26 @@ def compute_time_deltas(self, points): idea to re-run this function once an undistorted frame is obtained for better estimates. ''' - #points = self.undistort()#self.p_jt # provide in N x 3 x = points[:, 0] y = points[:, 1] - angles = np.arctan2(y, -x) # scanline starts at positive x axis and moves clockwise, we take midpoint pi/2 as 0 - dT = self.total_scan_time * angles / (2 * np.pi) - #dT -= self.total_scan_time / 2 # offset range, as defined in [-scan_time /2 , scan_time/2] - self.dT = dT - - def undistort(self, v_j): + # scanline starts at positive x axis and moves clockwise + # We take midpoint pi/2 as 0 + angles = np.arctan2(y, -x) + dT = period * angles / (2 * np.pi) + return dT + + @staticmethod + def undistort(v_j, points, period = 1 / RADAR_SCAN_FREQUENCY, times = None): ''' Computes a new set of undistorted observed points, based on the current best estimate of v_T, T_wj, dT ''' + if times is None: + assert(period > 0) + times = MotionDistortionSolver.compute_time_deltas(period, points) + v_j_column = np.expand_dims(v_j, axis = 1) - displacement = v_j_column * self.dT # 3 x N + displacement = v_j_column * times # 3 x N theta = displacement[2, :] # (N,) dx = displacement[0, :] # (N,) @@ -98,11 +138,10 @@ def undistort(self, v_j): T_j_jt = np.array([[np.cos(theta), -np.sin(theta), dx], [np.sin(theta), np.cos(theta), dy], [np.zeros(shape), np.zeros(shape), np.ones(shape)]]) - p_jt_col = np.expand_dims(self.p_jt, axis = 2) # N x 3 x 1 + p_jt_col = np.expand_dims(points, axis = 2) # N x 3 x 1 undistorted = T_j_jt.transpose((2, 0, 1)) @ p_jt_col # N x 3 x 1 return undistorted - # TODO: Check if this really should be inverted def expected_observed_pts(self, T_wj): ''' Returns the estimated positions of points based on their world location @@ -131,8 +170,7 @@ def error(self, v_j, T_wj): estimated observed positions and the velocity error. ''' # Compute point error - undistorted = self.undistort(v_j) - #self.compute_time_deltas(undistorted) + undistorted = MotionDistortionSolver.undistort(v_j, self.p_jt, times=self.dT) expected = self.expected_observed_pts(T_wj) naive_e_p = expected - np.squeeze(undistorted).T # 3 x N # Actual loss is the Cauchy robust loss, defined here: @@ -176,7 +214,7 @@ def jacobian(self, v_j, T_wj): J_v - gradient of point error and velocity error wrt velocity terms vx, vy, vtheta ''' - undistorted = self.undistort(v_j) + undistorted = MotionDistortionSolver.undistort(v_j, self.p_jt, times=self.dT) expected = self.expected_observed_pts(T_wj) input = expected - np.squeeze(undistorted).T # 3 x N naive_e_p = input[:2] @@ -248,7 +286,6 @@ def optimize_library(self): ''' Optimize using the LM implementation in the scipy library. ''' - self.compute_time_deltas(self.p_jt) # Initialize v, T T0 = self.T_wj_initial T_params = np.array([T0[0, 2], T0[1, 2], np.arctan2(T0[1, 0], T0[0, 0])]) diff --git a/trajectoryPlotting.py b/trajectoryPlotting.py index c8ced0b..3706c7e 100644 --- a/trajectoryPlotting.py +++ b/trajectoryPlotting.py @@ -58,6 +58,17 @@ def appendRelativeTransform(self, time, R, h): # new_pose = [*xy[0], *xy[1], self.poses[-1,2] + dth] self.poses = np.vstack((self.poses, new_pose)) + def appendAbsoluteTransform(self, time, pose): + ''' + @brief Append a relative transform to the trajectory + h should already be scaled by radar resolution + @param[in] time timestamp of the transform + @param[in] pose pose vector (3, ) + ''' + # Add to timestamps + self.timestamps = np.append(self.timestamps, time) + self.poses = np.vstack((self.poses, pose)) + def getPoseAtTimes(self, times): ''' @brief Given timestamps, return the pose at that time using cubic interpolation From 976875bb9eb587725b45668956a7ed8daff044dc Mon Sep 17 00:00:00 2001 From: liukeskywalker98 <57304384+liukeskywalker98@users.noreply.github.com> Date: Wed, 4 May 2022 03:18:48 -0400 Subject: [PATCH 34/37] A crap ton of hacking to get a working implementation. This code was not written with undistortion in mind. Had to break a few things to get a working version --- Mapping.py | 25 +++++++++++++++++---- RawROAMSystem.py | 30 +++++++++++++++++-------- Tracker.py | 13 ++++++++++- data/full_seq_1/INFO.md | 7 +++--- data/full_seq_1/gt/trajectory.png | Bin 991245 -> 0 bytes getFeatures.py | 3 +-- getTransformKLT.py | 3 ++- img/roam_mapping/tiny_traj/0001.jpg | Bin 80644 -> 62743 bytes motionDistortion.py | 33 +++++++++++++++++----------- 9 files changed, 80 insertions(+), 34 deletions(-) delete mode 100644 data/full_seq_1/gt/trajectory.png diff --git a/Mapping.py b/Mapping.py index 63066c2..18a15cc 100644 --- a/Mapping.py +++ b/Mapping.py @@ -62,7 +62,8 @@ def updateInfo(self, globalPose: np.ndarray, self.pointCloud = getPointCloudPolarInd(radarPolarImg) self.velocity = velocity - self.featurePointsLocalUndistorted = MotionDistortionSolver.undistort(velocity, featurePointsLocal) + print(featurePointsLocal.shape) + self.featurePointsLocalUndistorted = MotionDistortionSolver.undistort(velocity, featurePointsLocal)[:, :2] self.prunedUndistortedLocals = self.featurePointsLocalUndistorted def copyFromOtherKeyframe(self, keyframe) -> None: @@ -95,7 +96,23 @@ def convertFeaturesLocalToGlobal( return featurePointsGlobal def getPrunedFeaturesGlobalPosition(self): - return self.convertFeaturesLocalToGlobal(self.prunedUndistortedLocals) + x, y, th = self.pose + + # First translate local points to origin at center + featurePointsGlobal = self.prunedUndistortedLocals - RADAR_CART_CENTER + + # Then we need to convert to meters + #featurePointsGlobal *= RANGE_RESOLUTION_CART_M # px * (m/px) = m + + # Center origin at pose center + + # Rotate and translate to make into global coordinate system + R = getRotationMatrix(th) + t = np.array([x, y]).reshape(2, 1) + featurePointsGlobal = (R @ (featurePointsGlobal.T + t)).T + + return featurePointsGlobal + return self.convertFeaturesLocalToGlobal() def pruneFeaturePoints(self, corrStatus: np.ndarray) -> None: ''' @@ -103,8 +120,8 @@ def pruneFeaturePoints(self, corrStatus: np.ndarray) -> None: @param[in] corrStatus @note In place changing of `self.prunedFeaturePoints` function, which aims to track and prune away the feature points that are part of this keyframe ''' - self.prunedFeaturePoints = self.prunedFeaturePoints[corrStatus] - self.prunedUndistortedLocals = self.prunedUndistortedLocals[corrStatus] + self.prunedFeaturePoints = self.prunedFeaturePoints[corrStatus.flatten().astype(bool)] + self.prunedUndistortedLocals = self.prunedUndistortedLocals[corrStatus.flatten().astype(bool)] # def isVisible(self, keyframe): # ''' diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 0e5a02d..d227550 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -142,13 +142,14 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # Get initial features from Cartesian image blobCoord = np.empty((0, 2)) blobCoord, _ = appendNewFeatures(prevImgCart, blobCoord) + print(f"blobCoord shape:{blobCoord.shape}") # Initialize first keyframe zero_velocity = np.zeros((3,)) - old_kf = Keyframe(initPose, blobCoord, prevImgPolar, ) # pointer to previous kf + old_kf = Keyframe(initPose, blobCoord, prevImgPolar, zero_velocity) # pointer to previous kf self.map.addKeyframe(old_kf) - possible_kf = Keyframe(initPose, blobCoord, prevImgPolar) + possible_kf = Keyframe(initPose, blobCoord, prevImgPolar, zero_velocity) for seqInd in range(startSeqInd + 1, endSeqInd + 1): # Obtain polar and Cart image @@ -162,16 +163,18 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: blobCoord, seqInd) # Keyframe updating + print(f"Pruning index shape {corrStatus.shape}") old_kf.pruneFeaturePoints(corrStatus) print("Detected", np.rad2deg(rotAngleRad), "[deg] rotation") estR = getRotationMatrix(-rotAngleRad) - R, h = tracker.getTransform(good_old, good_new, pixel = False) + R, h = tracker.getTransform(good_old, good_new, pixel = True) # R = estR # Solve for Motion Compensated Transform - p_w = old_kf.getPrunedFeaturesGlobalPosition() + p_w = old_kf.getPrunedFeaturesGlobalPosition() + # Initial Transform guess T_wj = np.block([[R, h], [np.zeros((2,)), 1]]) @@ -179,13 +182,14 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: MDS.update_problem(prev_pose, p_w, good_new, T_wj) undistort_solution = MDS.optimize_library() pose_vector = undistort_solution[3:] + velocity = undistort_solution[:3] # Update trajectory #self.updateTrajectory(R, h, seqInd) self.updateTrajectoryAbsolute(pose_vector, seqInd) latestPose = pose_vector #self.estTraj.poses[-1] - possible_kf.updateInfo(latestPose, good_new, currImgPolar) + possible_kf.updateInfo(latestPose, good_new, currImgPolar, velocity) # Add a keyframe if it fulfills criteria # 1) large enough translation from previous keyframe @@ -195,7 +199,8 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # NOTE: Feature check and appending will only be checked in the next iteration, # so we can prematuraly do it here and add a keyframe first nFeatures = good_new.shape[0] - if (nFeatures <= N_FEATURES_BEFORE_RETRACK) or \ + retrack = (nFeatures <= N_FEATURES_BEFORE_RETRACK) + if retrack or \ self.map.isGoodKeyframe(possible_kf): print("\nAdding keyframe...\n") @@ -208,16 +213,23 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # Proposed fix: old_kf = possible_kf # switch ptr to new object # Initialize new poss_kf for new ptr old_kf = possible_kf - possible_kf = Keyframe(latestPose, good_new, currImgPolar) + # TODO: Never replenished blobCoord. Proposed fix: Done here. + if retrack: + print(f"Restocking features for new keyframe") + good_new, _ = appendNewFeatures(currImgCart, good_new) + old_kf.updateInfo(latestPose, good_new, currImgPolar, velocity) + print(f"{old_kf.featurePointsLocal.shape[0]} features now") + possible_kf = Keyframe(latestPose, blobCoord, currImgPolar, velocity) # TODO: do bundle adjustment here self.map.bundleAdjustment() # Plotting and prints and stuff - self.plot(prevImgCart, currImgCart, good_old, good_new, R, h, - seqInd) + # self.plot(prevImgCart, currImgCart, good_old, good_new, R, h, + # seqInd) # Update incremental variables blobCoord = good_new.copy() + print(f"Blob coord is now size: {blobCoord.shape}") prevImgCart = currImgCart prev_pose = convertPoseToTransform(latestPose) diff --git a/Tracker.py b/Tracker.py index 73450b0..79bf79e 100644 --- a/Tracker.py +++ b/Tracker.py @@ -82,12 +82,23 @@ def track( print( f"{seqInd} | Num good features: {nGoodFeatures} of {nFeatures} ({(nGoodFeatures / nFeatures) * 100:.2f}%) | Time: {toc(start):.2f}s" ) + + # plt.subplot(1, 2, 1) + # plt.scatter(good_old[:,0], good_old[:,1]) + # plt.subplot(1, 2, 2) + # plt.scatter(good_new[:,0], good_new[:,1]) + # plt.show() # Outlier rejection doOutlierRejection = self.paramFlags.get("rejectOutliers", True) if doOutlierRejection: good_old, good_new, pruning_mask = rejectOutliers(good_old, good_new) - + + # plt.subplot(1, 2, 1) + # plt.scatter(good_old[:,0], good_old[:,1]) + # plt.subplot(1, 2, 2) + # plt.scatter(good_new[:,0], good_new[:,1]) + # plt.show() # Ensure correct correspondence status rng = np.arange(nFeatures) corrStatus[rng[corrStatus.flatten().astype(bool)]] &= pruning_mask[:, np.newaxis] diff --git a/data/full_seq_1/INFO.md b/data/full_seq_1/INFO.md index f85f4b2..f73d0d9 100644 --- a/data/full_seq_1/INFO.md +++ b/data/full_seq_1/INFO.md @@ -11,7 +11,7 @@ Distance: 9.02 km Frames: 8867 FPS: 4.00 Hz Resolution: 4.32 cm -Range: 165 m +Range: 165 m ## Dataset Structure @@ -23,7 +23,6 @@ Range: 165 m |-- (bunch of radar images labelled as .png) |-- info.txt (this file) |-- radar.timestamps (in form: - |-- gt - |-- radar_odometry.csv (processed ground truth odometry data from GPS) - + |-- radar_odometry.csv (processed ground truth odometry data from GPS) + ``` diff --git a/data/full_seq_1/gt/trajectory.png b/data/full_seq_1/gt/trajectory.png deleted file mode 100644 index d1880a90f180396bcb7b72cb8cc88cde763bad2b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 991245 zcmW)ncTiK$*Tz#Qp?3&930)}>X;LIWfB@1JkS3u>2WirzM+`j_0YQ2IQJQo@M`;Su zdq<>5RhnNAeSLp_+}XQ#cIM37+1YdU^E@Zc$UvKpnu{6$0MO}R&?W!?m=*v42}3CU zefgtGSMYBD`k82}1FFC9?EagOIjiZb0RZn)XnteK|IMkMU>^Db0L=dX{{UAW^9KO{ ze@k`HYWIU|r>>o|h_k7sG(V+=E$c}-fC_c@lqXecw#Q{;#1^T491;3!Optq6!Q+0ra|Ernm1oK&&&OKHBn(9_-^P@bDuy{^i$Ilc2>Vw%p}eZZ!aGaxVg%Y!|g z*@NjNm!*IY#shS*p0oUoHjT#q73#xIpLNXlGL{UUeW>zY_DS7&tLOC8x+*mw$!w+1 zRLLvYVaRjetJP8R8`pA~lA`KUhWxw!4E&E;Kff~#e4_lcPj^9|LGf_YW3w#1bFqNl 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+N_FEATURES_BEFORE_RETRACK = 60 # TODO: Make it dynamic (find the overall loss) # TODO: Make dynamic? @@ -103,7 +103,6 @@ def appendNewFeatures(srcImg, oldFeaturesCoord): @param[in] srcImg Source image to obtain features on @param[in] oldFeaturesCoord (K x 2) array of [x, y] coordinate of features ''' - newFeatureCoord, newFeatureRadii = getFeatures(srcImg) print("Added", newFeatureCoord.shape[0], "new features!") diff --git a/getTransformKLT.py b/getTransformKLT.py index fb7babd..ce6b7e7 100644 --- a/getTransformKLT.py +++ b/getTransformKLT.py @@ -15,7 +15,7 @@ from utils import * PLOT_BAD_FEATURES = False -N_FEATURES_BEFORE_RETRACK = 80 +N_FEATURES_BEFORE_RETRACK = 60 def visualize_transform(prevImg: np.ndarray, currImg: np.ndarray, @@ -349,6 +349,7 @@ def getTrackedPointsKLT( if nFeatures < N_FEATURES_BEFORE_RETRACK: featurePtSrc, N_FEATURES_BEFORE_RETRACK = \ appendNewFeatures(srcImg, featurePtSrc) + print("WARNING: getTransformKLT added new features!") # Perform KLT to get 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0) times = MotionDistortionSolver.compute_time_deltas(period, points) @@ -140,7 +146,7 @@ def undistort(v_j, points, period = 1 / RADAR_SCAN_FREQUENCY, times = None): [np.zeros(shape), np.zeros(shape), np.ones(shape)]]) p_jt_col = np.expand_dims(points, axis = 2) # N x 3 x 1 undistorted = T_j_jt.transpose((2, 0, 1)) @ p_jt_col # N x 3 x 1 - return undistorted + return np.squeeze(undistorted) # N x 3 def expected_observed_pts(self, T_wj): ''' @@ -172,7 +178,7 @@ def error(self, v_j, T_wj): # Compute point error undistorted = MotionDistortionSolver.undistort(v_j, self.p_jt, times=self.dT) expected = self.expected_observed_pts(T_wj) - naive_e_p = expected - np.squeeze(undistorted).T # 3 x N + naive_e_p = expected - undistorted.T # 3 x N # Actual loss is the Cauchy robust loss, defined here: e_p_i = np.log(np.square(naive_e_p[:2, :]) / 2 + 1) # 2 x N e_p = e_p_i.flatten(order='F') @@ -185,13 +191,12 @@ def error(self, v_j, T_wj): dy = T_j_j1[1, 2] dtheta = np.arctan2(T_j_j1[1, 0], T_j_j1[0, 0]) v_j_prior = np.array([dx, dy, dtheta]) / self.total_scan_time - #print(f"Prior velocity: {v_j_prior}") v_diff = (v_j - v_j_prior) v_diff[2] = normalize_angles(v_diff[2]) e_v = v_diff * e_p_i.shape[1] # (3,) # ideally should warp2pi here on theta error e = np.hstack((e_p, e_v)) - #print(e) + return e def jacobian_vector(self, params): @@ -216,7 +221,7 @@ def jacobian(self, v_j, T_wj): ''' undistorted = MotionDistortionSolver.undistort(v_j, self.p_jt, times=self.dT) expected = self.expected_observed_pts(T_wj) - input = expected - np.squeeze(undistorted).T # 3 x N + input = expected - undistorted.T # 3 x N naive_e_p = input[:2] cauchy_derivative = naive_e_p / (np.square(naive_e_p) / 2 + 1) # 3 x N @@ -290,8 +295,9 @@ def optimize_library(self): T0 = self.T_wj_initial T_params = np.array([T0[0, 2], T0[1, 2], np.arctan2(T0[1, 0], T0[0, 0])]) initial_guess = np.hstack((self.v_j_initial, T_params)) - print(f"Initial v guess: {self.v_j_initial}") - print(f"Initial T guess: {T_params}") + if VERBOSE: + print(f"Initial v guess: {self.v_j_initial}") + print(f"Initial T guess: {T_params}") result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = '2-point', method = 'lm') # result = sp.optimize.least_squares(self.error_vector, initial_guess, jac = self.jacobian_vector, method = 'lm') @@ -305,9 +311,10 @@ def optimize_library(self): 2 : "ftol termination condition is satisfied", 3 : "xtol termination condition is satisfied", 4 : "Both ftol and xtol termination conditions are satisfied"} - print(f"Final v: {best_params[:3]}") - print(f"Final t: {best_params[3:]}") - print(f"Used {num_evals} evaluations") - print(f"Residuals were {result.fun}") - print(status_dict[status]) + if VERBOSE: + print(f"Final v: {best_params[:3]}") + print(f"Final t: {best_params[3:]}") + print(f"Used {num_evals} evaluations") + print(f"Residuals were {result.fun}") + print(status_dict[status]) return best_params From 59b04399a5246a8883590038e228725db84cb9bd Mon Sep 17 00:00:00 2001 From: liukeskywalker98 <57304384+liukeskywalker98@users.noreply.github.com> Date: Wed, 4 May 2022 05:34:43 -0400 Subject: [PATCH 35/37] Made various fixes to coordinate systems, plotting apparatus --- Mapping.py | 4 +-- RawROAMSystem.py | 41 +++++++++++++++++----------- img/roam_mapping/tiny_traj.mp4 | Bin 74565 -> 168895 bytes img/roam_mapping/tiny_traj/0001.jpg | Bin 62743 -> 51278 bytes img/roam_mapping/tiny_traj/0002.jpg | Bin 72596 -> 49808 bytes img/roam_mapping/tiny_traj/0003.jpg | Bin 72652 -> 50046 bytes img/roam_mapping/tiny_traj/0004.jpg | Bin 70961 -> 52633 bytes img/roam_mapping/tiny_traj/0005.jpg | Bin 71534 -> 52209 bytes img/roam_mapping/tiny_traj/0006.jpg | Bin 71384 -> 53744 bytes img/roam_mapping/tiny_traj/0007.jpg | Bin 72580 -> 55416 bytes img/roam_mapping/tiny_traj/0008.jpg | Bin 73682 -> 56206 bytes img/roam_mapping/tiny_traj/0009.jpg | Bin 77923 -> 56805 bytes img/roam_mapping/tiny_traj/0010.jpg | Bin 75450 -> 57139 bytes motionDistortion.py | 6 ++-- trajectoryPlotting.py | 2 +- 15 files changed, 32 insertions(+), 21 deletions(-) diff --git a/Mapping.py b/Mapping.py index 18a15cc..dae89a5 100644 --- a/Mapping.py +++ b/Mapping.py @@ -63,7 +63,7 @@ def updateInfo(self, globalPose: np.ndarray, self.velocity = velocity print(featurePointsLocal.shape) - self.featurePointsLocalUndistorted = MotionDistortionSolver.undistort(velocity, featurePointsLocal)[:, :2] + self.featurePointsLocalUndistorted = MotionDistortionSolver.undistort(velocity, featurePointsLocal-RADAR_CART_CENTER)[:, :2] self.prunedUndistortedLocals = self.featurePointsLocalUndistorted def copyFromOtherKeyframe(self, keyframe) -> None: @@ -99,7 +99,7 @@ def getPrunedFeaturesGlobalPosition(self): x, y, th = self.pose # First translate local points to origin at center - featurePointsGlobal = self.prunedUndistortedLocals - RADAR_CART_CENTER + featurePointsGlobal = self.prunedUndistortedLocals # Then we need to convert to meters #featurePointsGlobal *= RANGE_RESOLUTION_CART_M # px * (m/px) = m diff --git a/RawROAMSystem.py b/RawROAMSystem.py index d227550..13b9b83 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -12,6 +12,9 @@ from motionDistortion import MotionDistortionSolver from utils import * +# Bad solution. better solution is to save in config between mapping and this file +RADAR_CART_CENTER = np.array([1012, 1012]) + class RawROAMSystem(): def __init__(self, @@ -173,15 +176,21 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # R = estR # Solve for Motion Compensated Transform - p_w = old_kf.getPrunedFeaturesGlobalPosition() - + p_w = old_kf.getPrunedFeaturesGlobalPosition() # centered + centered_new = good_new - RADAR_CART_CENTER # Initial Transform guess - T_wj = np.block([[R, h], - [np.zeros((2,)), 1]]) + T_wj = prev_pose @ np.block([[R, h], + [np.zeros((2,)), 1]]) - MDS.update_problem(prev_pose, p_w, good_new, T_wj) + # Give Motion Distort info on two new frames + MDS.update_problem(prev_pose, p_w, centered_new, T_wj) undistort_solution = MDS.optimize_library() + + # Extract new info pose_vector = undistort_solution[3:] + transform = convertPoseToTransform(pose_vector) + R = transform[:2, :2] + h = transform[:2, 2:] velocity = undistort_solution[:3] # Update trajectory @@ -224,8 +233,8 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: self.map.bundleAdjustment() # Plotting and prints and stuff - # self.plot(prevImgCart, currImgCart, good_old, good_new, R, h, - # seqInd) + self.plot(prevImgCart, currImgCart, good_old, good_new, R, h, + seqInd, show = True) # Update incremental variables blobCoord = good_new.copy() @@ -274,7 +283,7 @@ def plot(self, ax2 = self.fig.add_subplot(1, 2, 2) # TODO: Plotting for map points - self.map.plot(self.fig, show=False) + #self.map.plot(self.fig, show=False) self.plotTraj(seqInd, R, h, save=False, show=False) @@ -286,14 +295,14 @@ def plot(self, self.fig.savefig(trajSavePathInd) # # Save by subplot - # if save: - # imgSavePath = self.filePaths["imgSave"] - # imgSavePathInd = os.path.join(imgSavePath, f"{seqInd:04d}.jpg") - # plt_savefig_by_axis(imgSavePathInd, self.fig, ax1) + if save: + imgSavePath = self.filePaths["imgSave"] + imgSavePathInd = os.path.join(imgSavePath, f"{seqInd:04d}.jpg") + plt_savefig_by_axis(imgSavePathInd, self.fig, ax1) - # trajSavePath = self.filePaths["trajSave"] - # trajSavePathInd = os.path.join(trajSavePath, f"{seqInd:04d}.jpg") - # plt_savefig_by_axis(trajSavePathInd, self.fig, ax2) + trajSavePath = self.filePaths["trajSave"] + trajSavePathInd = os.path.join(trajSavePath, f"{seqInd:04d}.jpg") + plt_savefig_by_axis(trajSavePathInd, self.fig, ax2) if show: plt.pause(0.01) @@ -371,7 +380,7 @@ def plotTraj(self, seqInd, R, h, save=False, show=False): print("Generating mp4 with script (requires bash and FFMPEG command)...") try: # Save video sequence - os.system(f"./img/mp4-from-folder.sh {imgSavePath} {startSeqInd + 1}") + os.system(f"./img/mp4-from-folder.sh 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398b3cc..19f3255 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -1,6 +1,7 @@ import numpy as np import scipy as sp from utils import * +import matplotlib.pyplot as plt #sp.linalg.sqrtm #sp.lin @@ -46,7 +47,7 @@ def __init__(self, T_wj0, p_w, p_jt, T_wj, sigma_p, sigma_v, self.total_scan_time = 1 / frequency # e_v Parameters - self.v_j_initial = self.infer_velocity(T_wj) + self.v_j_initial = self.infer_velocity(self.T_wj0_inv @ T_wj) # Initial velocity guess (prior velocity/ velocity from SVD solution) # Optimization parameters @@ -84,7 +85,7 @@ def update_problem(self, T_wj0, p_w, p_jt, T_wj): self.T_wj_initial = T_wj # e_v Parameters - self.v_j_initial = self.infer_velocity(T_wj) + self.v_j_initial = self.infer_velocity(self.T_wj0_inv @ T_wj) # Initial velocity guess (prior velocity/ velocity from SVD solution) self.dT = MotionDistortionSolver.compute_time_deltas(self.total_scan_time, p_jt) @@ -179,6 +180,7 @@ def error(self, v_j, T_wj): undistorted = MotionDistortionSolver.undistort(v_j, self.p_jt, times=self.dT) expected = self.expected_observed_pts(T_wj) naive_e_p = expected - undistorted.T # 3 x N + # Actual loss is the Cauchy robust loss, defined here: e_p_i = np.log(np.square(naive_e_p[:2, :]) / 2 + 1) # 2 x N e_p = e_p_i.flatten(order='F') diff --git a/trajectoryPlotting.py b/trajectoryPlotting.py index 3706c7e..4e0687e 100644 --- a/trajectoryPlotting.py +++ b/trajectoryPlotting.py @@ -127,7 +127,7 @@ def plotGtAndEstTrajectory(gtTraj, title='GT and EST Trajectories', info=None, savePath=None, - arrow=True): + arrow=False): ''' @brief Plot ground truth trajectory and estimated trajectory @param[in] gtTrajectory Ground truth trajectory From e7d4d0c7e9e8cdb53327f2a1b4d324435445cf2c Mon Sep 17 00:00:00 2001 From: liukeskywalker98 <57304384+liukeskywalker98@users.noreply.github.com> Date: Wed, 4 May 2022 06:11:31 -0400 Subject: [PATCH 36/37] Left TODO comments for tomorrow's work --- Mapping.py | 2 +- RawROAMSystem.py | 10 ++++++---- motionDistortion.py | 4 +++- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/Mapping.py b/Mapping.py index dae89a5..4de920c 100644 --- a/Mapping.py +++ b/Mapping.py @@ -102,7 +102,7 @@ def getPrunedFeaturesGlobalPosition(self): featurePointsGlobal = self.prunedUndistortedLocals # Then we need to convert to meters - #featurePointsGlobal *= RANGE_RESOLUTION_CART_M # px * (m/px) = m + featurePointsGlobal *= RANGE_RESOLUTION_CART_M # px * (m/px) = m # Center origin at pose center diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 13b9b83..4c347fa 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -5,7 +5,7 @@ from Mapping import Keyframe, Map from getFeatures import N_FEATURES_BEFORE_RETRACK, appendNewFeatures -from parseData import convertPolarImageToCartesian, getCartImageFromImgPaths, getPolarImageFromImgPaths, getRadarImgPaths +from parseData import convertPolarImageToCartesian, getCartImageFromImgPaths, getPolarImageFromImgPaths, getRadarImgPaths, RANGE_RESOLUTION_CART_M from trajectoryPlotting import Trajectory, getGroundTruthTrajectory, plotGtAndEstTrajectory from utils import convertRandHtoDeltas, f_arr, getRotationMatrix, plt_savefig_by_axis, radarImgPathToTimestamp from Tracker import Tracker @@ -172,12 +172,14 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: print("Detected", np.rad2deg(rotAngleRad), "[deg] rotation") estR = getRotationMatrix(-rotAngleRad) - R, h = tracker.getTransform(good_old, good_new, pixel = True) + R, h = tracker.getTransform(good_old, good_new, pixel = False) # R = estR # Solve for Motion Compensated Transform p_w = old_kf.getPrunedFeaturesGlobalPosition() # centered - centered_new = good_new - RADAR_CART_CENTER + #TODO: Scatter p_w, then try the transform on the centered new points + # Scatter that on the same plot + centered_new = (good_new - RADAR_CART_CENTER) * RANGE_RESOLUTION_CART_M # Initial Transform guess T_wj = prev_pose @ np.block([[R, h], [np.zeros((2,)), 1]]) @@ -188,7 +190,7 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # Extract new info pose_vector = undistort_solution[3:] - transform = convertPoseToTransform(pose_vector) + transform = MDS.T_wj0_inv @ convertPoseToTransform(pose_vector) R = transform[:2, :2] h = transform[:2, 2:] velocity = undistort_solution[:3] diff --git a/motionDistortion.py b/motionDistortion.py index 19f3255..917f41b 100644 --- a/motionDistortion.py +++ b/motionDistortion.py @@ -23,6 +23,8 @@ TODO: Run parse data to look at how the scanline moves. Adjust code to match TODO: Verify code against math TODO: Integration with the code. Adding mapping capability +TODO: Check arctan2 conventions for dT stuff. the image might be flipped because +images have the origin in the top left. Plan: Check out parse data @@ -114,7 +116,7 @@ def compute_time_deltas(period, points): ''' x = points[:, 0] y = points[:, 1] - # scanline starts at positive x axis and moves clockwise + # scanline starts at positive x axis and moves clockwise (counter-clockwise?) # We take midpoint pi/2 as 0 angles = np.arctan2(y, -x) dT = period * angles / (2 * np.pi) From d4f7f3eb4e0fa5714a8871c0c450d4a4e593467b Mon Sep 17 00:00:00 2001 From: liukeskywalker98 <57304384+liukeskywalker98@users.noreply.github.com> Date: Wed, 4 May 2022 21:03:50 -0400 Subject: [PATCH 37/37] Working state. Two bugs in Mapping.py: the global feature extraction, which has an aliasing bug, and a miscomputation of a transform R(x + t) instead of Rx + t. Code is messy but operable. Results are good, but the computation is slow. Areas to improve: rework Keyframe object to allow null initialization to avoid pointless point cloud generation. Push all math into transforms when working in the machine, and only convert to parameter vectors when necessary for human readability. --- Mapping.py | 9 ++-- RawROAMSystem.py | 76 ++++++++++++++++++++-------- img/roam_mapping/tiny_traj/0001.jpg | Bin 51278 -> 67301 bytes img/roam_mapping/tiny_traj/0002.jpg | Bin 49808 -> 65917 bytes img/roam_mapping/tiny_traj/0003.jpg | Bin 50046 -> 66889 bytes img/roam_mapping/tiny_traj/0004.jpg | Bin 52633 -> 65932 bytes img/roam_mapping/tiny_traj/0005.jpg | Bin 52209 -> 65196 bytes img/roam_mapping/tiny_traj/0006.jpg | Bin 53744 -> 66251 bytes img/roam_mapping/tiny_traj/0007.jpg | Bin 55416 -> 69927 bytes img/roam_mapping/tiny_traj/0008.jpg | Bin 56206 -> 69895 bytes img/roam_mapping/tiny_traj/0009.jpg | Bin 56805 -> 72553 bytes img/roam_mapping/tiny_traj/0010.jpg | Bin 57139 -> 33800 bytes motionDistortion.py | 5 +- parseData.py | 1 - 14 files changed, 61 insertions(+), 30 deletions(-) diff --git a/Mapping.py b/Mapping.py index 4de920c..42b6c16 100644 --- a/Mapping.py +++ b/Mapping.py @@ -62,8 +62,7 @@ def updateInfo(self, globalPose: np.ndarray, self.pointCloud = getPointCloudPolarInd(radarPolarImg) self.velocity = velocity - print(featurePointsLocal.shape) - self.featurePointsLocalUndistorted = MotionDistortionSolver.undistort(velocity, featurePointsLocal-RADAR_CART_CENTER)[:, :2] + self.featurePointsLocalUndistorted = MotionDistortionSolver.undistort(velocity, featurePointsLocal)[:, :2] self.prunedUndistortedLocals = self.featurePointsLocalUndistorted def copyFromOtherKeyframe(self, keyframe) -> None: @@ -91,7 +90,7 @@ def convertFeaturesLocalToGlobal( # Rotate and translate to make into global coordinate system R = getRotationMatrix(th) t = np.array([x, y]).reshape(2, 1) - featurePointsGlobal = (R @ (featurePointsGlobal.T + t)).T + featurePointsGlobal = (R @ (featurePointsGlobal.T) + t).T return featurePointsGlobal @@ -102,17 +101,15 @@ def getPrunedFeaturesGlobalPosition(self): featurePointsGlobal = self.prunedUndistortedLocals # Then we need to convert to meters - featurePointsGlobal *= RANGE_RESOLUTION_CART_M # px * (m/px) = m # Center origin at pose center # Rotate and translate to make into global coordinate system R = getRotationMatrix(th) t = np.array([x, y]).reshape(2, 1) - featurePointsGlobal = (R @ (featurePointsGlobal.T + t)).T + featurePointsGlobal = (R @ (featurePointsGlobal.T) + t).T return featurePointsGlobal - return self.convertFeaturesLocalToGlobal() def pruneFeaturePoints(self, corrStatus: np.ndarray) -> None: ''' diff --git a/RawROAMSystem.py b/RawROAMSystem.py index 4c347fa..44c3097 100644 --- a/RawROAMSystem.py +++ b/RawROAMSystem.py @@ -14,7 +14,7 @@ # Bad solution. better solution is to save in config between mapping and this file RADAR_CART_CENTER = np.array([1012, 1012]) - +wantToPlot = -1 class RawROAMSystem(): def __init__(self, @@ -145,14 +145,14 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # Get initial features from Cartesian image blobCoord = np.empty((0, 2)) blobCoord, _ = appendNewFeatures(prevImgCart, blobCoord) - print(f"blobCoord shape:{blobCoord.shape}") # Initialize first keyframe + metricCoord = (blobCoord - RADAR_CART_CENTER) * RANGE_RESOLUTION_CART_M zero_velocity = np.zeros((3,)) - old_kf = Keyframe(initPose, blobCoord, prevImgPolar, zero_velocity) # pointer to previous kf + old_kf = Keyframe(initPose, metricCoord, prevImgPolar, zero_velocity) # pointer to previous kf self.map.addKeyframe(old_kf) - possible_kf = Keyframe(initPose, blobCoord, prevImgPolar, zero_velocity) + possible_kf = Keyframe(initPose, metricCoord, prevImgPolar, zero_velocity) for seqInd in range(startSeqInd + 1, endSeqInd + 1): # Obtain polar and Cart image @@ -164,9 +164,19 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: good_old, good_new, rotAngleRad, corrStatus = tracker.track( prevImgCart, currImgCart, prevImgPolar, currImgPolar, blobCoord, seqInd) - + ''' + if seqInd == wantToPlot: + plt.figure() + plt.subplot(1, 2, 1) + plt.scatter(good_old[:,0], good_old[:,1]) + plt.subplot(1, 2, 2) + plt.title("Good old") + #applied = homogenize(centered_new) @ new_transform.T + plt.scatter(good_new[:,0], good_new[:,1]) + plt.title(f"Good new") + plt.show() + ''' # Keyframe updating - print(f"Pruning index shape {corrStatus.shape}") old_kf.pruneFeaturePoints(corrStatus) print("Detected", np.rad2deg(rotAngleRad), "[deg] rotation") @@ -177,6 +187,7 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: # Solve for Motion Compensated Transform p_w = old_kf.getPrunedFeaturesGlobalPosition() # centered + #TODO: Scatter p_w, then try the transform on the centered new points # Scatter that on the same plot centered_new = (good_new - RADAR_CART_CENTER) * RANGE_RESOLUTION_CART_M @@ -185,22 +196,44 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: [np.zeros((2,)), 1]]) # Give Motion Distort info on two new frames - MDS.update_problem(prev_pose, p_w, centered_new, T_wj) + debug = False + if seqInd == wantToPlot: + debug = False + # Centered_new is in meters, p_w is in meters, T_wj is in meters, prev_pose is meters + MDS.update_problem(prev_pose, p_w, centered_new, T_wj, debug) undistort_solution = MDS.optimize_library() # Extract new info pose_vector = undistort_solution[3:] - transform = MDS.T_wj0_inv @ convertPoseToTransform(pose_vector) - R = transform[:2, :2] - h = transform[:2, 2:] + new_transform = convertPoseToTransform(pose_vector) + relative_transform = MDS.T_wj0_inv @ new_transform + ''' + if seqInd == wantToPlot: + plt.figure() + plt.subplot(1, 3, 1) + plt.scatter(p_w[:,0], p_w[:,1]) + plt.subplot(1, 3, 2) + plt.title("World coordinates") + applied = homogenize(centered_new) @ new_transform.T + plt.scatter(centered_new[:,0], centered_new[:,1]) + plt.title(f"Post-Transform: {(np.max(centered_new[:,0]) - np.min(centered_new[:,0]))/(np.max(p_w[:,0]) - np.min(p_w[:,0]))}") + plt.subplot(1, 3, 3) + diff = p_w - applied[:, :2] + plt.scatter(diff[:,0], diff[:,1]) + plt.show() + ''' + R = relative_transform[:2, :2] + h = relative_transform[:2, 2:] velocity = undistort_solution[:3] + #velocity[:2] /= RANGE_RESOLUTION_CART_M # Update trajectory #self.updateTrajectory(R, h, seqInd) self.updateTrajectoryAbsolute(pose_vector, seqInd) latestPose = pose_vector #self.estTraj.poses[-1] - possible_kf.updateInfo(latestPose, good_new, currImgPolar, velocity) + # Good new is given in pixels, given velocity in meters, uh oh, pose in meters + possible_kf.updateInfo(latestPose, centered_new, currImgPolar, velocity) # Add a keyframe if it fulfills criteria # 1) large enough translation from previous keyframe @@ -217,7 +250,7 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: print("\nAdding keyframe...\n") #old_kf.copyFromOtherKeyframe(possible_kf) - self.map.addKeyframe(possible_kf) + #self.map.addKeyframe(possible_kf) # TODO: Aliasing above? old_kf is never assigned the object possible_kf, # map ends up with a list of N pointers to the same keyframe @@ -226,21 +259,22 @@ def run(self, startSeqInd: int = 0, endSeqInd: int = -1) -> None: old_kf = possible_kf # TODO: Never replenished blobCoord. Proposed fix: Done here. if retrack: - print(f"Restocking features for new keyframe") good_new, _ = appendNewFeatures(currImgCart, good_new) - old_kf.updateInfo(latestPose, good_new, currImgPolar, velocity) - print(f"{old_kf.featurePointsLocal.shape[0]} features now") - possible_kf = Keyframe(latestPose, blobCoord, currImgPolar, velocity) + centered_new = (good_new - RADAR_CART_CENTER) * RANGE_RESOLUTION_CART_M + old_kf.updateInfo(latestPose, centered_new, currImgPolar, velocity) + possible_kf = Keyframe(latestPose, centered_new, currImgPolar, velocity) # TODO: do bundle adjustment here - self.map.bundleAdjustment() + #self.map.bundleAdjustment() # Plotting and prints and stuff - self.plot(prevImgCart, currImgCart, good_old, good_new, R, h, - seqInd, show = True) - + if seqInd == endSeqInd: + self.plot(prevImgCart, currImgCart, good_old, good_new, R, h, + seqInd, save = True, show = False) + else: + self.plot(prevImgCart, currImgCart, good_old, good_new, R, h, + seqInd, save = False, show = False) # Update incremental variables blobCoord = good_new.copy() - print(f"Blob coord is now size: {blobCoord.shape}") prevImgCart = currImgCart prev_pose = convertPoseToTransform(latestPose) diff --git a/img/roam_mapping/tiny_traj/0001.jpg b/img/roam_mapping/tiny_traj/0001.jpg index 3a6e83df200cf64a9cba0d2ce3716e7ea5f75f75..b2a5ce8a794243bf85eb5dc0333447da070cfd5e 100644 GIT binary patch literal 67301 zcmd?R1yG#L*C+bm5S-v1oZ#-31PeifySux)yA#~qf(`Bt!QCB#y9B-Ses|@2-*0#S zyZ6@C)~!2JT{Tlo_tU>Vr%xaGHE)Y=n*iEZF-b810s;as1iyi|72q=f4+{$i3j+@a z2Zw+F{|*Tg83_>)2@m5vDkc#=2{92qAt5ORBQ+@*JvkvE4HqpvGYdOAI|((905>Z? zBO5#GUxPp(ARr(iBH06@Ti zXZzQL|I-Ts5(*jy77iZa9U}M#4QK!)1QZk`G!zUBG&K0vUf{n2(C9E2A6P_S-^=U4 zk=bFg`o-nKlYgq|#!{HNpkULt_eXe#jf0DaPf0~hL;I1PgOiJ!hxhXrQ894|$*A)>6zp4+mw!R~H)Q{F!2JIoA^R`D{wuC!00{~LEIcT5KnS>hq{;Jr_rKac5~Pn> zu%Ndz1w+tBU>d6+wl8ienv_!K9GP896Sc8QvxgDNm%6y`tt_J=0Jwi{2y-RCFbtlaTa%#Umur8Hf(h`_m!ep-H~dbIQZE_ 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