LidarCameraCalibration

Table of Contents
References
Test approach of Autoware
Test approach of existing ROS Code

References

[1] An existing code using ROS and PCL library
https://github.com/heethesh/lidar_camera_calibration

[2] An another ROS pkg,
http://wiki.ros.org/but_calibration_camera_velodyne
https://github.com/robofit/but_velodyne

[3] An Matlab reference code
https://github.com/YechengLyu/WPI-LiDAR-Camera-Calibration-Toolbox

[4] Autoware code
https://autoware.readthedocs.io/en/feature-documentation_rtd/DevelopersGuide/PackagesAPI/sensing/autoware_camera_lidar_calibrator.html
https://ai4sig.org/2018/07/docker-for-autoware/
https://github.com/CPFL/Autoware-Manuals/blob/master/en/Autoware_UsersManual_v1.1.md

Autoware with docker

Short summary:

The code can be fully run, and both camera intrinsic and camera-LIDAR extrinsic can be calculated
Verification of calibration result is still pending

Install autoware via docker

Assuming docker and nvidia-docker runtime have been installed
Following steps as below
https://gitlab.com/autowarefoundation/autoware.ai/autoware/wikis/Generic-x86-Docker#run-an-autoware-docker-image

Run autoware calibration

The code below uses example data set for illustration purpose.

in 1st terminal

cd docker/generic
./build.sh
roscore

in 2nd terminal

cd docker/generic
./build.sh
cd ~/shared_dir/
rosbag play -l new_rectified.bag

in 3rd terminal

cd docker/generic
./build.sh
source /opt/ros/kinetic/setup.bash
rosrun rviz rviz

in 4th terminal

cd docker/generic
./build.sh
rosrun autoware_camera_lidar_calibrator cameracalibrator.py --square 0.05 --size 7x5 image:=/sensors/camera/image_color

camera intrinsic; the corners will be selected automatically

roslaunch autoware_camera_lidar_calibrator camera_lidar_calibration.launch intrinsics_file:=/home/autoware/20190711_0256_autoware_camera_calibration.yaml image_src:=/sensors/camera/image_color

camera-lidar extrinsic; pick up 4 corners in each image and point cloud pair alternatively

in the image window, click chessboard corner
in rviz, tick "Publish Point", then zoom/rotate/translate to find the corresponding point inside point cloud and select it

until the transformation matrix is generated and the values are stable (in theory, the more image-lidar pairs are ticked, the more accurate for transformation values).

Verify the calculated transformation

by projecting point cloud into image frame

Close the autoware_camera_lidar_calibrator node in Terminal 4, and continue with the following

rosrun calibration_publisher calibration_publisher  image_topic_name:=/sensors/camera/image_color

in rviz started by Terminal 3, select Panels –> Add New Panel –> ImageViewerPlugin，and then in the ImageViewerPlugin sub window, tick select Image Topic & Point Topics

in 5th terminal

cd docker/generic
./build.sh
rosrun points2image points2image _points_node:=/sensors/velodyne_points

The reason to run calibration_publisher and points2image manually as above is due to when launching them from the autoware runtime manager, options are missing or error occurs.

In case by default calibration_publisher does not use the latest calibration yaml file, run the following

rosparam set /calibration_publisher/calibration_file /home/autoware/20190716_083429_autoware_lidar_camera_calibration.yaml

In case calibration_publisher does not publish /projection_matrix topic as expected, run the following

rosparam set /calibration_publisher/publish_extrinsic_mat true

Test approach of Reference [1] with docker

Short summary: the full code can be run following all the steps, yet the testing result of this approach is not stable - the calibration matrix value vibrates a lot everytime after a new image/pointcloud pair is newly registed.

create a new network foo using the network command

docker network create foo

Give docker the rights to access the X-Server, by running below in terminal

xhost +local:docker

in 1st terminal

nvidia-docker run \
              -it --rm \
              --net foo \
              --name master \
              ros:kinetic-perception-xenial \
              roscore

in 2nd terminal

nvidia-docker run \
              -it --rm \
              --net foo \
              --name talker \
              -v /tmp/.X11-unix:/tmp/.X11-unix \
              --env DISPLAY=unix$DISPLAY \
              --env ROS_HOSTNAME=talker \
              --env ROS_MASTER_URI=http://master:11311 \
              -v $(pwd)/calibration/:/opt/ros/kinetic/share/catkin_ws/ \
              ros:kinetic-perception-xenial

cd $ROS_PACKAGE_PATH/catkin_ws/
catkin_make
source devel/setup.bash
roslaunch lidar_camera_calibration play_rosbag.launch

in 3rd terminal

enter the active container,

docker exec -it talker bash

install dependancies inside container,

apt-get update &&\
apt-get install -y ros-kinetic-ros-numpy \
                ros-kinetic-tf2-sensor-msgs \
                python-tk \
                python-pip &&\
pip install --upgrade pip &&\
python -m pip install -U matplotlib // this will install version 2.x

source /opt/ros/kinetic/share/catkin_ws/devel/setup.bash

run calib

rosrun lidar_camera_calibration calibrate_camera_lidar.py --calibrate

ROS Docker reference

[1] https://hub.docker.com/_/ros

radeonwu / lidarcameracalibration Goto Github PK