ARS-SLAM: Accurate Robust Spinning LiDAR SLAM for a Quadruped Robot in Large-Scale Scenario

Jiehao Li, Chenglin Li, Hongkai Chen, Haijun Guo, Xiwen Luo, C. L. Philip Chen, Chenguang Yang

Abstract: It is challenging to employ a quadruped robot for real-time mapping and positioning in a large range of scenes. The significant vibration and instability of the quadruped robot during mobility, as well as the quantity of computation required to convey a wide variety of complex landscapes, result in unsatisfactory drawing construction accuracy and inefficient real-time performance. Therefore, we propose an accurate robust spinning LiDAR SLAM (ARS-SLAM) algorithm for a quadruped robot under the large-scale scene. The tightly coupled iterative Kalman filter in FAST-LIO2 is introduced into the front end of the cartographer framework to improve the accuracy and robustness of robot pose estimation. To reduce the computational complexity of the original cartographer framework, a pose threshold optimization algorithm was introduced to effectively remove redundant information from loop detection and improve computational efficiency and real-time performance. We tested the system's performance against the most advanced point-cloud-based methods, LIO-SAM and FAST-LIO2, on a large dataset of large science parks and underground parking lots, and the results show that the proposed system achieves the same or better accuracy and real-time performance.

External IDs:dblp:conf/icra/LiLCGLCY25