Wheel-Legged SLAM: Indoor LiDAR-Inertial SLAM Integrating Kinematic Model of Wheel-Legged Robots

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.

Authors

Institutions

Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2024-12-23
DOI
https://doi.org/10.1109/lra.2024.3521184
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Wheel-Legged SLAM: Indoor LiDAR-Inertial SLAM Integrating Kinematic Model of Wheel-Legged Robots

Lijin Han, Chaoyang Jiang, Hui Liu, Ying Li et al.
1 citations
IEEE Robotics and Automation Letters
Robotic Path Planning Algorithms
0.25
article

Wheel-Legged SLAM: Indoor LiDAR-Inertial SLAM Integrating Kinematic Model of Wheel-Legged Robots

Lijin Han, Chaoyang Jiang, Hui Liu, Ying Li, Yifan Zhang, Yan Wen, Hongyu Hou, Qingyi Shang
article en
1 citations

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.

IEEE Robotics and Automation LettersVol. 10(2)
Beijing Institute of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 43%
Robotic Path Planning Algorithms
0.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.