不确定条件下相机和激光雷达内、外统一标定

Julius Kummerle, Tilman Kuhner
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引用次数: 22

摘要

文献中提出了许多相机和激光雷达校准方法,但没有一种方法可以同时估计所有的内在和外在参数,因此在概率意义上是最优的。在这项工作中,我们提出了一种在统一问题中同时估计相机和激光雷达的内在和外在参数的方法。我们推导了一个概率公式,使不同测量类型的完美集成无需手动调整权重。可以同时校准任意数量的摄像机和激光雷达。测量不需要时间同步。该方法适用于任何相机模型。在评估中,我们表明额外的LiDAR测量显着改善了相机的固有校准。此外,我们在实际数据上表明,我们的方法达到了最先进的校准精度和高可靠性。
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Unified Intrinsic and Extrinsic Camera and LiDAR Calibration under Uncertainties
Many approaches for camera and LiDAR calibration are presented in literature but none of them estimates all intrinsic and extrinsic parameters simultaneously and therefore optimally in a probabilistic sense.In this work, we present a method to simultaneously estimate intrinsic and extrinsic parameters of cameras and LiDARs in a unified problem. We derive a probabilistic formulation that enables flawless integration of different measurement types without hand-tuned weights. An arbitrary number of cameras and LiDARs can be calibrated simultaneously. Measurements are not required to be time-synchronized. The method is designed to work with any camera model.In evaluation, we show that additional LiDAR measurements significantly improve intrinsic camera calibration. Further, we show on real data that our method achieves state-of-the-art calibration precision with high reliability.
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