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

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

References in corpus (2)

• Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping
• IN2LAAMA: INertial Lidar Localisation Autocalibration And MApping

Cited by in corpus (4)

• FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
• Ground-SLAM: Ground Constrained LiDAR SLAM for Structured Multi-Floor Environments
• StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks
• BALM: Bundle Adjustment for Lidar Mapping