HCTO: Optimality-Aware LiDAR Inertial Odometry with Hybrid Continuous Time Optimization for Compact Wearable Mapping System
<|reference_start|>HCTO: Optimality-Aware LiDAR Inertial Odometry with Hybrid Continuous Time Optimization for Compact Wearable Mapping System: Compact wearable mapping system (WMS) has gained significant attention due to their convenience in various applications. Specifically, it provides an efficient way to collect prior maps for 3D structure inspection and robot-based "last-mile delivery" in complex environments. However, vibrations in human motion and the uneven distribution of point cloud features in complex environments often lead to rapid drift, which is a prevalent issue when applying existing LiDAR Inertial Odometry (LIO) methods on low-cost WMS. To address these limitations, we propose a novel LIO for WMSs based on Hybrid Continuous Time Optimization (HCTO) considering the optimality of Lidar correspondences. First, HCTO recognizes patterns in human motion (high-frequency part, low-frequency part, and constant velocity part) by analyzing raw IMU measurements. Second, HCTO constructs hybrid IMU factors according to different motion states, which enables robust and accurate estimation against vibration-induced noise in the IMU measurements. Third, the best point correspondences are selected using optimal design to achieve real-time performance and better odometry accuracy. We conduct experiments on head-mounted WMS datasets to evaluate the performance of our system, demonstrating significant advantages over state-of-the-art methods. Video recordings of experiments can be found on the project page of HCTO: \href{https://github.com/kafeiyin00/HCTO}{https://github.com/kafeiyin00/HCTO}.<|reference_end|>
@article{li2024hcto:,
    title={HCTO: Optimality-Aware LiDAR Inertial Odometry with Hybrid Continuous
  Time Optimization for Compact Wearable Mapping System},
    author={Jianping Li, Shenghai Yuan, Muqing Cao, Thien-Minh Nguyen, Kun Cao,
  Lihua Xie},
    journal={arXiv preprint arXiv:2403.14173},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14173},
    primaryClass={cs.RO}
}