Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV
This work addresses a domain-specific problem for autonomous vehicles, providing incremental improvements in LOAM performance.
The paper tackled the problem of LiDAR odometry and mapping for LiDARs with small field-of-view and irregular samplings, achieving better precision and efficiency compared to existing baselines in a robust, real-time algorithm.
LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot's pose and build high-precision, high-resolution maps of the surrounding environment. This enables autonomous navigation and safe path planning of autonomous vehicles. In this paper, we present a robust, real-time LOAM algorithm for LiDARs with small FoV and irregular samplings. By taking effort on both front-end and back-end, we address several fundamental challenges arising from such LiDARs, and achieve better performance in both precision and efficiency compared to existing baselines. To share our findings and to make contributions to the community, we open source our codes on Github