TY - GEN
T1 - Error Propagation Mechanism for the 2.5-D Grid Map Update in LiDAR Gaze Control Applications for Omni-Directional Wheeled Robots
AU - Yang, Mengshen
AU - JIA, Fuhua
AU - Rushworth, Adam
AU - Sun, Xu
AU - Fang, Zaojun
AU - Yang, Guilin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - This paper presents an actively gaze controlled LiDAR system for the omni-directional wheeled robot, with this system, the LiDAR odometry drift caused by rapid rotation and feature degradation can be greatly mitigated. This is an extension of our previous work [1]. In our previous work, information from the point cloud are extracted and projected to a 2.5-D grid map, this grid map acts as an indicator for the distributions of the feature points, thus can be used to guide the LiDAR to choose an optimal gaze angle. However, errors and randomness inside the map are not accounted and quantified. In this paper, we investigate the mechanisms of error propagation during map building, and derive the formulas for map updates using an 1-D Kalman filter. Several simulations are conducted to verify the usefulness and practicability of the proposed system with an omni-directional robot.
AB - This paper presents an actively gaze controlled LiDAR system for the omni-directional wheeled robot, with this system, the LiDAR odometry drift caused by rapid rotation and feature degradation can be greatly mitigated. This is an extension of our previous work [1]. In our previous work, information from the point cloud are extracted and projected to a 2.5-D grid map, this grid map acts as an indicator for the distributions of the feature points, thus can be used to guide the LiDAR to choose an optimal gaze angle. However, errors and randomness inside the map are not accounted and quantified. In this paper, we investigate the mechanisms of error propagation during map building, and derive the formulas for map updates using an 1-D Kalman filter. Several simulations are conducted to verify the usefulness and practicability of the proposed system with an omni-directional robot.
KW - Kalman filter mapping
KW - LiDAR SLAM
KW - Omni-dirctional robot odometry
UR - http://www.scopus.com/inward/record.url?scp=85218469486&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-0774-7_18
DO - 10.1007/978-981-96-0774-7_18
M3 - Conference contribution
AN - SCOPUS:85218469486
SN - 9789819607730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 243
EP - 259
BT - Intelligent Robotics and Applications - 17th International Conference, ICIRA 2024, Proceedings
A2 - Lan, Xuguang
A2 - Mei, Xuesong
A2 - Jiang, Caigui
A2 - Zhao, Fei
A2 - Tian, Zhiqiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Intelligent Robotics and Applications, ICIRA 2024
Y2 - 31 July 2024 through 2 August 2024
ER -