Liu Wanli
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Sensors (Basel). 2017 Mar 8;17(3):539. doi: 10.3390/s17030539.
The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.
激光雷达（LiDAR）与惯性测量单元（IMU）之间的时间延迟校准是其应用的一项基本前提条件。然而，激光雷达与IMU测量值之间的对应关系通常是未知的，因此无法直接计算用于时间延迟校准。为了解决激光雷达-IMU时间延迟校准问题，本文提出了一种基于迭代最近点（ICP）和迭代西格玛点卡尔曼滤波器（ISPKF）的融合方法，该方法结合了ICP和ISPKF的优点。ICP算法能够精确确定激光雷达-IMU之间的未知变换；而ISPKF算法能够最优地估计时间延迟校准参数。首先，实现从激光雷达坐标系到IMU坐标系的坐标变换。其次，建立激光雷达和IMU的测量模型以及时间延迟误差模型。第三，给出用于激光雷达-IMU时间延迟校准的ICP和ISPKF程序的方法。给出的实验结果验证了所提方法，并证明时间延迟误差能够被精确校准。
