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arXiv · 2402.02337

Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association

Thien-Minh Nguyen·Xinhang Xu·Tongxing Jin·Yizhuo Yang·Jianping Li·Shenghai Yuan·Lihua Xie
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Abstract

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.

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Thien-Minh Nguyen, Xinhang Xu, Tongxing Jin, Yizhuo Yang, Jianping Li, Shenghai Yuan, Lihua Xie. 2024-02-04. Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association. https://doi.org/10.1109/lra.2024.3391049

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