A Calibration Algorithm of 3-D Point Cloud Acquisition System Based on KMPE Cost Function

For the three-dimensional (3D) point cloud acquisition system, accurately estimating the measurement model parameters is essential, so a calibration algorithm based on the kernel mean p -power error (KMPE) cost function for the 3D point cloud acquisition system is proposed. Firstly, a space sphere with unknown radius is chosen as the calibration target. Secondly, since the KMPE cost function is insensitive to measurement noise and outliers, we establish a KMPE cost function-based nonlinear optimization model for the measurement model parameters by using the geometric constraints of the scanning points on the sphere. Thirdly, combining the successful history-based differential evolution parameter adaptation (SHADE) algorithm with the Levenberg-Marquardt (LM) algorithm to optimize the optimization model, and the optimal measurement model parameters can be obtained. Experimental findings confirm that the 3D point cloud acquisition system can be calibrated precisely by using the proposed algorithm, which can also significantly reduce the impact of the measurement noise and outliers.