A SLAM System with Direct Velocity Estimation for Mechanical and Solid-State LiDARs

Simultaneous localization and mapping (SLAM) is essential for intelligent robots operating in unknown environments. However, existing algorithms are typically developed for specific types of solid-state LiDARs, leading to weak feature representation abilities for new sensors. Moreover, LiDAR-based SLAM methods are limited by distortions caused by LiDAR ego motion. To address the above issues, this paper presents a versatile and velocity-aware LiDAR-based odometry and mapping (VLOM) system. A spherical projection-based feature extraction module is utilized to process the raw point cloud generated by various LiDARs, hence avoiding the time-consuming adaptation of various irregular scan patterns. The extracted features are grouped into higher-level clusters to filter out smaller objects and reduce false matching during feature association. Furthermore, bundle adjustment is adopted to jointly estimate the poses and velocities for multiple scans, effectively improving the velocity estimation accuracy and compensating for point cloud distortions. Experiments on publicly available datasets demonstrate the superiority of VLOM over other state-of-the-art LiDAR-based SLAM systems in terms of accuracy and robustness. Additionally, the satisfactory performance of VLOM on RS-LiDAR-M1, a newly released solid-state LiDAR, shows its applicability to a wide range of LiDARs.

Automated summary

This paper presents a versatile and velocity-aware LiDAR-based odometry and mapping (VLOM) system, which uses a spherical projection-based feature extraction method to process raw point clouds from various LiDARs and improve feature representation. Bundle adjustment is adopted to jointly estimate poses and velocities, improving velocity estimation accuracy and compensating for point cloud distortions. Experimental results demonstrate the superiority of VLOM in terms of accuracy and robustness.