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

TL;DR

This paper introduces Loam_livox, a real-time, high-precision LiDAR odometry and mapping system optimized for small FoV LiDARs, improving robustness and efficiency for autonomous vehicle navigation.

Contribution

The paper presents a novel LOAM algorithm tailored for small FoV LiDARs, addressing unique challenges and outperforming existing methods in accuracy and speed.

Findings

Enhanced precision in LiDAR odometry for small FoV sensors

Improved computational efficiency over baseline methods

Open-sourced code for community use

Abstract

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