📝 Update docs

* update building and running instructions;
* fix some typos.

Signed-off-by: Luiz Carlos Cosmi Filho <[email protected]>

Author: luizcarloscf

README.md

@@ -20,30 +20,36 @@ Exploration is done using just a simple controller `turtlebot3_explorer` based o
 
 ### Occupancy grid
 
-The entire solution proposed here for counting obstacles is based on the use of an occupancy grid map. To generate this map, it was developed a ROS2 node that subscribes to receive messages from the laser sensor and updates the occupancy grid map for each message received. Initially, all points on the occupancy map have probability equal to 50%. As messages are received from the laser sensor, occupied points will have probability above 50% and free points on the map will have probability below 50%. This probabilistic occupancy grid is published at a fixed rate in the `/custom_map` topic. 
+The entire solution proposed here for counting obstacles is based on the use of an occupancy grid map. To generate this map, it was developed a ROS2 node `turtlebot3_occupancy_grid` that subscribes to receive messages from the laser sensor and updates the occupancy grid map for each message received. Initially, all points on the occupancy map have probability equal to 50%. As messages are received from the laser sensor, occupied points will have probability above 50% and free points on the map will have probability below 50%. This probabilistic occupancy grid is published at a fixed rate in the `/custom_map` topic. 
 
 
 <p align="center">
     <img src="etc/images/turtlebot3_occupancy_grid.png" alt="turtlebot3_occupancy_grid" width="400"/>
 </p>
 
 
-The occupancy grid mapping algorithm ses the log-odds representation of occupancy:
+The occupancy grid mapping algorithm uses the log-odds representation of occupancy:
 
 $$l_{t,i} = log(\frac{p(m_i|z_{1:t},x_{1:t})}{1 - p(m_i|z_{1:t},x_{1:t})})$$
 
 The probabilities are easily recovered from the log-odds ratio:
 
 $$p(m_i|z_{1:t},x_{1:t}) = 1 - \frac{1}{1+ exp(l_{t,i})}$$
 
-The algorithm occupancy grid mapping in below loops through all grid cells $i$, and updates those that were measured. The function `inverse_sensor_model` implements the inverse measurement model $p(m_i|z_{1:t},x_{1:t})$ in its log-odds form: if it measured any smaller than the maximum laser range, then mark the points on the map that are under the laser beam as free and the last one as occupied; if it measured some infinite value, truncated to max laser range and marks all as free grid cells.
+The algorithm occupancy grid mapping in below loops through all grid cells $i$, and updates those that were measured. The function `inverse_sensor_model` implements the inverse measurement model $p(m_i|z_{1:t},x_{1:t})$ in its log-odds form: if it measured any smaller than the maximum laser range, then mark the points on the map that are under the laser beam as free and the last one as occupied; if it measured some infinite value, truncated to max laser range and marks all as free grid cells. To accomplish this, an implementation of the [Bresenham line drawing algorithm](https://en.wikipedia.org/wiki/Bresenham%27s_line_algorithm) is used.
 
 <p align="center">
     <img src="etc/images/occupancy_grid_algo.png" alt="occupancy_grid" width="400"/>
 </p>
 
 ### Detect obstacles/objects
 
+
+<p align="center">
+    <img src="https://scipy-lectures.org/_images/sphx_glr_plot_labels_001.png" alt="connected_components" width="400"/>
+</p>
+
+
 <p align="center">
     <img src="etc/images/turtlebot3_object_detector.png" alt="turtlebot3_object_detector" width="400"/>
 </p>
@@ -58,24 +64,42 @@ The algorithm occupancy grid mapping in below loops through all grid cells $i$,
 
 ## Building
 
+You can build all packages needed to run this assignment with docker:
 ```bash
-docker build -t assignment-1 -f etc/docker/Dockerfile .
+docker build -t assignment-1:latest -f etc/docker/Dockerfile .
 ```
 
 ## Runnning
 
+Not really safe, but it works.
 ```bash
 sudo xhost +local:root
 ```
 
+Then, create a network to run all containers in it:
 ```bash
 docker network create my-net
 ```
 
+Then, open a terminal in the container with support for the QT application:
+```bash
+docker run -it -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix:rw --device /dev/dri/ --net=my-net assignment-1:latest /bin/bash
+```
+
+The first thing you'll need to do is run the gazebo inside the container:
+```bash
+gazebo
+```
+
+We haven't figured out why yet, but the first launch of the gazebo inside the container takes a long time. After the gazebo has opened the first time, you can close it and run our launch.
 ```bash
-docker run -it -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix:rw --device /dev/dri/ --net=my-net assignment-1 /bin/bash
+ros2 launch turtlebot3_mapper turtlebot3_mapper_launch.py
 ```
 
+If you want to explore the environment, just open a new container in the same network and run the action client node.
 ```bash
-docker run -it --net=my-net assignment-1 /bin/bash
+docker run -it --net=my-net assignment-1:latest /bin/bash
+ros2 run turtlebot3_mapper turtlebot3_mission_client -f 200
 ```
+
+After the task is finished, you can view the results in the generated `results.txt` file.