classical-SLAM

一些经典的SLAM算法学习并注释的版本

Google cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

• cartographer
• cartographer-ROS
• cartographer-README

ORB-SLAM 框架

• ORB-SLAM

• ORB-SLAM2

ORB-SLAM2代码
特点： tracking, local mapping, loop closing 三线程

• ORB-SLAM2with_pointcloud_map

ORB-SLAM2with_pointcloud_map代码
特点： 在tracking, local mapping, loop closing 三线程的基础上,增加了semantic segmentation, dense mapping threads.

• DS-SLAM(A Semantic Visual SLAM towards Dynamic Environments)

论文 代码
特点：在ORB-SLAM2with_pointcloud_map的基础上，增加了SegNet(pixel-wise semantic segmentation based on caffe in real-time)

VINS 框架

• VINS-Fusion

VINS-Fusion代码 VINS-Fusion is an optimization-based multi-sensor state estimator, which achieves accurate self-localization for autonomous applications (drones, cars, and AR/VR). VINS-Fusion is an extension of VINS-Mono, which supports multiple visual-inertial sensor types (mono camera + IMU, stereo cameras + IMU, even stereo cameras only). We also show a toy example of fusing VINS with GPS

MSCKF 框架

• MSCKF_VIO EKF-based VIO approach

The MSCKF_VIO package is a stereo version of MSCKF. The software takes in synchronized stereo images and IMU messages and generates real-time 6DOF pose estimation of the IMU frame.

• [SR-ISWF -an extension of MSCKF]