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LiDAR odometry and mapping (LOAM) has been playing an important role inautonomous vehicles, due to its ability to simultaneously localize the robot'spose and build high-precision, high-resolution maps of the surroundingenvironment. This enables autonomous navigation and safe path planning ofautonomous vehicles. In this paper, we present a robust, real-time LOAMalgorithm for LiDARs with small FoV and irregular samplings. By taking efforton both front-end and back-end, we address several fundamental challengesarising from such LiDARs, and achieve better performance in both precision andefficiency compared to existing baselines. To share our findings and to makecontributions to the community, we open source our codes on Github

Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV