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arXiv · 2408.04901

CTE-MLO: Continuous-time and Efficient Multi-LiDAR Odometry with Localizability-aware Point Cloud Sampling

Hongming Shen·Zhenyu Wu·Yulin Hui·Wei Wang·Qiyang Lyu·Tianchen Deng·Yeqing Zhu·Bailing Tian·Danwei Wang
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Abstract

In recent years, LiDAR-based localization and mapping methods have achieved significant progress thanks to their reliable and real-time localization capability. Considering single LiDAR odometry often faces hardware failures and degeneracy in practical scenarios, Multi-LiDAR Odometry (MLO), as an emerging technology, is studied to enhance the performance of LiDAR-based localization and mapping systems. However, MLO can suffer from high computational complexity introduced by dense point clouds that are fused from multiple LiDARs, and the continuous-time measurement characteristic is constantly neglected by existing LiDAR odometry. This motivates us to develop a Continuous-Time and Efficient MLO, namely CTE-MLO, which can achieve accurate and real-time estimation using multi-LiDAR measurements through a continuous-time perspective. In this paper, the Gaussian process estimation is naturally combined with the Kalman filter, which enables each LiDAR point in a point stream to query the corresponding continuous-time trajectory using its time instants. A decentralized multi-LiDAR synchronization scheme is also devised to combine points from separate LiDARs into a single point cloud without the primary LiDAR assignment. Moreover, with the aim of improving the real-time performance of MLO without sacrificing robustness, a point cloud sampling strategy is designed with the consideration of localizability. To this end, CTE-MLO integrates synchronization, localizability-aware sampling, continuous-time estimation, and voxel map management within a Kalman filter framework, which can achieve high accuracy and robust continuous-time estimation within only a few linear iterations. The effectiveness of the proposed method is demonstrated through various scenarios, including public datasets and real-world applications. The code is available at https://github.com/shenhm516/CTE-MLO to benefit the community.

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Hongming Shen, Zhenyu Wu, Yulin Hui, Wei Wang, Qiyang Lyu, Tianchen Deng, Yeqing Zhu, Bailing Tian, Danwei Wang. 2025-02-14. CTE-MLO: Continuous-time and Efficient Multi-LiDAR Odometry with Localizability-aware Point Cloud Sampling. https://arxiv.org/abs/2408.04901

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