  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
