Wheel-Legged SLAM: Indoor LiDAR-Inertial SLAM Integrating Kinematic Model of Wheel-Legged Robots
Yan Wen, Ying Li, Qingyi Shang, Chaoyang Jiang, Hongyu Hou, Hui Liu, Yifan Zhang, Lijin Han
Abstract
SLAM is the key technique for localization and surrounding perception in indoor environments. However, the dynamic posture adjustments of wheel-legged robots cast new challenges that affect the accuracy of localization. Therefore, this letter presents the Wheel-Legged SLAM, a novel indoor SLAM method for wheel-legged robots. The contributions of this research include the effective fusion of a dual LiDAR system for enhancing the robustness in degenerative indoor settings, the vertical correction factor for reducing the Z-axis drift and the gravity re-estimation factor for improving the pose accuracy. Extensive experiments in various indoor environments, including factories, corridors, and underground garages, demonstrate the robustness and accuracy of the proposed approach. The results indicate that the method proposed in this letter enhances the robustness of the LiDAR-inertial SLAM indoors, significantly improves the localization accuracy and reduces accumulative errors for the wheel-legged robot.
BibTeX
@inproceedings{ral2025_wheelleggedslami,
title = {Wheel-Legged SLAM: Indoor LiDAR-Inertial SLAM Integrating Kinematic Model of Wheel-Legged Robots},
author = {Yan Wen and Ying Li and Qingyi Shang and Chaoyang Jiang and Hongyu Hou and Hui Liu and Yifan Zhang and Lijin Han},
booktitle = {RA-L 2025},
year = {2025}
}