Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association
<|reference_start|>Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association: In this paper, we propose a continuous-time lidar-inertial odometry (CT-LIO) system named SLICT2, which promotes two main insights. One, contrary to conventional wisdom, CT-LIO algorithm can be optimized by linear solvers in only a few iterations, which is more efficient than commonly used nonlinear solvers. Two, CT-LIO benefits more from the correct association than the number of iterations. Based on these ideas, we implement our method with a customized solver where the feature association process is performed immediately after each incremental step, and the solution can converge within a few iterations. Our implementation can achieve real-time performance with a high density of control points while yielding competitive performance in highly dynamical motion scenarios. We demonstrate the advantages of our method by comparing with other existing state-of-the-art CT-LIO methods. The source code will be released for the benefit of the community.<|reference_end|>
@article{nguyen2024eigen,
    title={Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry
  with Internal Association},
    author={Thien-Minh Nguyen, Xinhang Xu, Tongxing Jin, Yizhuo Yang, Jianping Li,
  Shenghai Yuan, Lihua Xie},
    journal={arXiv preprint arXiv:2402.02337},
    year={2024},
    doi={10.1109/LRA.2024.3391049},
    archivePrefix={arXiv},
    eprint={2402.02337},
    primaryClass={cs.RO}
}