Versatile LiDAR-Inertial Odometry With SE (2) Constraints for Ground Vehicles
<|reference_start|>Versatile LiDAR-Inertial Odometry With SE (2) Constraints for Ground Vehicles: LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM mainly leverage one specific constraint to improve the performance, e.g., information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics. In many robotic applications, these conditions are often known partially, hence a SLAM system can be a comprehensive problem due to the existence of numerous constraints. Therefore, we can achieve a better SLAM result by fusing them properly. In this paper, we propose a hybrid LiDAR-inertial SLAM framework that leverages both the on-board perception system and prior information such as motion dynamics to improve localization performance. In particular, we consider the case for ground vehicles, which are commonly used for autonomous driving and warehouse logistics. We present a computationally efficient LiDAR-inertial odometry method that directly parameterizes ground vehicle poses on SE(2). The out-of-SE(2) motion perturbations are not neglected but incorporated into an integrated noise term of a novel SE(2)-constraints model. For odometric measurement processing, we propose a versatile, tightly coupled LiDAR-inertial odometry to achieve better pose estimation than traditional LiDAR odometry. Thorough experiments are performed to evaluate our proposed method's performance in different scenarios, including localization for both indoor and outdoor environments. The proposed method achieves superior performance in accuracy and robustness.<|reference_end|>
@article{chen2023versatile,
    title={Versatile LiDAR-Inertial Odometry With SE (2) Constraints for Ground
  Vehicles},
    author={Jiaying Chen, Han Wang, Minghui Hu and Ponnuthurai Nagaratnam
  Suganthan},
    journal={IEEE Robotics and Automation Letters 2023},
    year={2023},
    doi={10.1109/LRA.2023.3268584},
    archivePrefix={arXiv},
    eprint={2404.01584},
    primaryClass={cs.RO}
}