@inproceedings{c4cf18b60bd64d418a81f29896167679,
title = "Tightly-Coupled LiDAR-inertial Odometry for Wheel-based Skid Steering UGV",
abstract = "Skid steering Unmanned Ground Vehicles (UGVs) are highly maneuverable and flexible and widely used in challenging urban environments. A variety of sensors are equipped so that they can realize autonomous localization and navigation. Therefore, the application of multi-sensor fusion SLAM in UGVs has become a research hotpot. This paper firstly proposes the extended Kalman filter to fuse IMU and wheel odometry by kinematics model, and the output state is fused with tightly coupled Lidar-inertial odometry to establish a lidar based SLAM system. Then, raw sensor data are collected on {"}DUBHE,{"}a skid steering UGV. Finally, by fusing different sensors for path drawing and comparison, the results show that the trajectory of our designed fusion system is closer to the ground truth. This paper is experimentally verified that our proposed algorithm integrating wheel odometer by kinematic model has good performance in the localization of skid steering UGV.",
keywords = "EKF, LiDAR-inertial Odometry, skid steering UGV",
author = "Mengkai Li and Lei Wang and Wenhu Ren and Qi Liu and Chaoyang Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022 ; Conference date: 16-12-2022 Through 19-12-2022",
year = "2022",
doi = "10.1109/ICIEA54703.2022.10006021",
language = "English",
series = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "510--516",
editor = "Wenxiang Xie and Shibin Gao and Xiaoqiong He and Xing Zhu and Jingjing Huang and Weirong Chen and Lei Ma and Haiyan Shu and Wenping Cao and Lijun Jiang and Zeliang Shu",
booktitle = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",
address = "United States",
}