A Comparison of LiDAR Odometry and Mapping Techniques
Published in 2021 IEEE 5th Colombian Conference on Automatic Control (CCAC), 2021
Abstract
Light detection and ranging LiDAR systems onboard mobile platforms are in rapid advancement for real-time mapping applications. Modern 3D laser scanners have a high data rate which, coupled with the complexity of their processing methods, makes simultaneous online localization and mapping (SLAM) a computational challenge. Different 3D LiDAR SLAM algorithms have emerged in recent years, most notably LiDAR Odometry and Mapping and its derivatives.
This paper performs the implementation of A-LOAM, ISCLOAM, and LeGO-LOAM algorithms and a respective comparison with the total sequences of the KITTI database which includes different environments and routes from a Velodyne HDL-64E sensor. The results evaluate the performance of the methods on computational cost, absolute error, and relative error.
Code implementation: https://github.com/HaroldMurcia/LOaM-comparison
Conference Details
- Conference: 2021 IEEE 5th Colombian Conference on Automatic Control (CCAC)
- Date: 19-22 October 2021
- Location: Ibagué, Colombia
- DOI: 10.1109/CCAC51819.2021.9633299
- Pages: 192-197
Keywords
Simultaneous localization and mapping, LiDAR, ROS, KITTI database, real-time pose estimation, point cloud compression, location awareness, laser radar, three-dimensional displays
Recommended citation: H. F. Murcia and C. F. Rubio, "A Comparison of LiDAR Odometry and Mapping Techniques," 2021 IEEE 5th Colombian Conference on Automatic Control (CCAC), Ibague, Colombia, 2021, pp. 192-197, doi: 10.1109/CCAC51819.2021.9633299.
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