Effective Solid State LiDAR Odometry Using Continuous-time Filter Registration

Abstract

Solid-state LiDARs are more compact and cheaper than the conventional mechanical multi-line spinning LiDARs, which have become increasingly popular in autonomous driving recently. However, there are several challenges for these new LiDAR sensors, including severe motion distortions, small field of view and sparse point cloud, which hinder them from being widely used in LiDAR odometry. To tackle these problems, we present an effective continuous-time LiDAR odometry (ECTLO) method for the Risley prism-based LiDARs with non-repetitive scanning patterns. To account for the noisy data, a filter-based point-to-plane Gaussian Mixture Model is used for robust registration. Moreover, a LiDAR-only continuous-time motion model is employed to relieve the inevitable distortions. To facilitate the implicit data association in parallel, we maintain all map points within a single range image. Extensive experiments have been conducted on various testbeds using the solid-state LiDARs with different scanning patterns, whose promising results demonstrate the efficacy of our proposed approach.

3D LiDAR SLAM: A survey

“…• Handcrafted SLAM frameworks (Hess et al, 2016;Shan & Englot, 2018), (Dellenbach et al, 2022;Ji & Singh, 2017;Lin & Zhang, 2020;Wang, Wang, & Xie, 2021) consist of well-established modules, serve as current mainstream solutions, and form a strong foundation for multi-sensor fusion. However, the system's maintenance and updates pose challenges, particularly in light of emerging sensors and hardware (Zheng & Zhu, 2022), necessitating ongoing algorithmic improvements. The rapid evolution of 3D computer vision has partly propelled learning-based odometry frameworks (Chen, Wang, et al, 2021;Zhou et al, 2023), which primarily focus on feature extraction and matching rather than providing a complete solution.…”

“…It incorporates a range-adaptive technique for robustly estimating local surface normals, merging points and normals using a rapid memory-efficient model update scheme. As shown in Figure8(b), ECTLO(Zheng & Zhu, 2022) develops an efficient odometry method for solid-state LiDARs, utilizing a point-to-plane GMM for registration, implementing a continuous-time motion model to mitigate distortions, and keeping all map points within a single range image to enable implicit data association in parallel.…”

“…As can be seen, R2LIVE is a hybrid method of filtering and factor graph optimization. To reduce the amount of computation, FAST-LIVO(Zheng et al, 2022) (Figure16c) directly registers original LiDAR F I G U R E 1 6 Three LIV LiDAR SLAM methods. (a-c) are originally shown inShao et al (2019),Lin & Zhang (2022a) andZheng et al (2022), respectively.…”

“…To reduce the amount of computation, FAST-LIVO(Zheng et al, 2022) (Figure16c) directly registers original LiDAR F I G U R E 1 6 Three LIV LiDAR SLAM methods. (a-c) are originally shown inShao et al (2019),Lin & Zhang (2022a) andZheng et al (2022), respectively. the LIO module and uses a semi-direct method for visual tracking in the VIO module, avoiding the feature extraction and description process in the whole system.…”