In this paper, we propose a robust point cloud registration method for ground vehicles. Given the vast developments in the field of autonomous vehicles, the use of point cloud data has increased. The simultaneous localization and mapping (SLAM) algorithm is typically used to generate sophisticated point cloud maps. In the SLAM algorithm, the quality of the map depends on the performance of loop closure algorithms. The iterative closest point (ICP) algorithm is widely used for loop closure of the point cloud. However, the ICP algorithm might not work well for ground vehicles because it was originally developed for 3D reconstruction in computer vision field. Therefore, this paper proposes a method to find a robust matching correspondences in the ICP algorithm on ground vehicle conditions. The performance of the proposed method is compared with other conventional methods by using KITTI open datasets.
The code is tested successfully at
- Linux 16.04 LTS
- ROS Kinetic
Date: 22/FEB/2019
Version : 0.0.2
Note: master branch
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Initial point cloud
Cyan: target point cloud
Red: initial point cloud
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G-ICP result (comparison method)
Cyan: target point cloud
Yellow: G-ICP point cloud
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GP-ICP result (Propsed method)
Cyan: target point cloud
Magenta: GP-ICP point cloud
Compile with 'catkin_make'
rosrun gpicp gpicp_test (you should run at the same folder with files velodyneCloud_1.pcd, ~_2.pcd)
Check the result with rviz (load 'rviz_conf.rivz)
The code is under BSD-License.
Any suggestions or improvements are welcome. Feel free to contact me at hjkim86@kaist.ac.kr.
Urban Robotics Lab (http://urobot.kaist.ac.kr)