Accurate and reliable sensor calibration is essential to fuse LiDAR and inertial measurements, which are usually available in robotic applications. In this paper, we propose a novel LiDAR-IMU calibration method within the continuous-time batch-optimization framework, where the intrinsics of both sensors and the spatial-temporal extrinsics between sensors are calibrated without using calibration infrastructure such as fiducial tags. Compared to discrete-time approaches, the continuous-time formulation has natural advantages for fusing high rate measurements from LiDAR and IMU sensors. To improve efficiency and address degenerate motions, two observability-aware modules are leveraged: (i) The information-theoretic data selection policy selects only the most informative segments for calibration during data collection, which significantly improves the calibration efficiency by processing only the selected informative segments. (ii) The observability-aware state update mechanism in nonlinear least-squares optimization updates only the identifiable directions in the state space with truncated singular value decomposition (TSVD), which enables accurate calibration results even under degenerate cases where informative data segments are not available. The proposed LiDAR-IMU calibration approach has been validated extensively in both simulated and real-world experiments with different robot platforms, demonstrating its high accuracy and repeatability in commonly-seen human-made environments. We also open source our codebase to benefit the research community: {\url{https://github.com/APRIL-ZJU/OA-LICalib}}.
翻译:精度和可靠的传感器校准对于整合LiDAR和惯性测量至关重要,这些测量通常在机器人应用中提供。在本文件中,我们建议在连续时间批量优化框架内采用新型的LiDAR-IMU校准方法,即传感器和传感器之间的空间时空外部校准的内在部分在校准时不使用校准基础设施(如光学标记)进行校准。与离线时间方法相比,连续时间配制在使用来自LiDAR和IMU传感器的高率测量方面自然具有优势。为了提高效率和应对变质动作,将两个可观测模块加以利用:(一) 信息理论数据选择政策只选择在数据收集过程中进行校准的最丰富的部分,即传感器和传感器之间的空间时空外外外外外校准。 (二) 非线性最低质量的惯性状态更新机制仅更新了州空间可识别的方向,其调定值为单值的重复降解状态(TSVD),这可以使精确的AR-awarebal-A模块社区在常规数据平台下获得精确校准结果。