Year: 2022
Type: conference-paper
Abstract: LiDAR odometry can achieve accurate vehicle pose estimation for short driving range or in small-scale environments, but for long driving range or in large-scale environments, the accuracy deteriorates as a result of cumulative estimation errors. This drawback necessitates the inclusion of loop closure detection in a SLAM framework to suppress the adverse effects of cumulative errors. To improve the accuracy of pose estimation, we propose a new LiDAR-based SLAM method which uses F-LOAM as LiDAR odometry, Scan Context for loop closure detection, and GTSAM for global optimization. In our approach, an adaptive distance threshold (instead of a fixed threshold) is employed for loop closure detection, which achieves more accurate loop closure detection results. Besides, a feature-based matching method is used in our approach to compute vehicle pose transformations between loop closure point cloud pairs, instead of using the raw point cloud obtained by the LiDAR sensor, which significantly reduces the computation time. The KITTI dataset is used for verifications of our method, and the experimental results demonstrate that the proposed method outperforms typical LiDAR odometry/SLAM methods in the literature. Our code is made publicly available for the benefit of the community11https://github.com/SlamCabbage/Optimized-SC-F-LOAM.
Authors: Lizhou Liao, Chunyun Fu, Binbin Feng, Tian Su
Institution: Chongqing University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4311214743