CTE-MLO: Continuous-Time and Efficient Multi-LiDAR Odometry With Localizability-Aware Point Cloud Sampling

In recent years, LiDAR-based localization and mapping methods have achieved significant progress, thanks to their reliable and real-time localization capability. Considering that 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 (CTE-MLO), which can achieve accurate and real-time estimation using multi-LiDAR measurements through a continuous-time perspective. In this article, 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. Exhaustive benchmarks on public datasets show that CTE-MLO is demonstratively competitive compared to other state-of-the-art (SOTA) methods in accuracy, robustness, and efficiency. Furthermore, CTE-MLO has undergone comprehensive validation across various scenarios (such as port, campus, and forest) and on diverse platforms [a full-size truck, an autonomous sweeper, and an agile micro aerial vehicle (MAV)], covering a total test area exceeding 3.6 km2, which affirm the applicability of CTE-MLO in industrial and field scenarios. A demonstration video is available at https://youtu.be/Q29PGPitHUI, and the code is also available at https://github.com/shenhm516/CTE-MLO to benefit the community.