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.
@article{arxiv.2402.02337,
title = {Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association},
author = {Thien-Minh Nguyen and Xinhang Xu and Tongxing Jin and Yizhuo Yang and Jianping Li and Shenghai Yuan and Lihua Xie},
journal= {arXiv preprint arXiv:2402.02337},
year = {2024}
}