Abstract
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.
Highlights
In today’s world, Light Detection and Ranging (LiDAR) devices and Inertial Measurement Units (IMUs) often found on vehicles, airplanes and robots are increasingly being used to perform localization or for navigation tasks
The methodology of the iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF) procedure is presented for LiDAR-IMU time delay calibration
In GPS-denied environments the IMUs are usually unreliable with respect to position for long periods of time due to time drift, which may cause large cumulative errors, the a LiDAR is a device which uses laser beams to determine the distance and azimuth from the sensor to an object, which can provide 3D localization information with high accuracy and efficiency to reduce or bound IMUs drift
Summary
In today’s world, Light Detection and Ranging (LiDAR) devices and Inertial Measurement Units (IMUs) often found on vehicles, airplanes and robots are increasingly being used to perform localization or for navigation tasks. In GPS-denied environments the IMUs are usually unreliable with respect to position for long periods of time due to time drift, which may cause large cumulative errors, the a LiDAR is a device which uses laser beams to determine the distance and azimuth from the sensor to an object, which can provide 3D localization information with high accuracy and efficiency to reduce or bound IMUs drift. The integrated LiDAR-IMU system can provide highly accurate position and pose information over long periods of time in GPS-denied environments. The LiDAR and IMU sensors must be properly time delay calibrated, including estimates of the relative timing of each sensor measurement, coordinate transformation between the different sensors, and time delay calibration parameters estimation are required [1,2,3,4]
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