A Robust LiDAR-IMU Joint Calibration Method

Current mainstream LiDAR-IMU (inertial measurement unit) joint calibration methods are of low accuracy in complex environments where there are serious occlusions or insufficient large planar surfaces. Facing that problem, a robust LiDAR-IMU joint calibration method is proposed. Firstly, line features are introduced in the matching construction stage, for they are not susceptive to occlusions and are of high localization accuracy. Both line feature matching pairs and planar patch matching pairs are constructed to strengthen the calibration constraint. Secondly, a two-stage optimization pipeline is constructed in the iterative optimization stage, where adaptive loss weights are designed according to the geometric residuals for each round of iterative optimization. Thus excellent convergence can be achieved by the optimization process, and the accuracy of the calibration method is improved. The proposed method is tested with the open-source outdoor dataset and the self-built indoor dataset. The results show that the calibration standard deviation of the proposed method for translation external parameters is about 2 mm and the calibration standard deviation for rotating external parameters is about 0.04?, which is much better than the state-of-the-art methods.