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

For the three-dimensional (3-D) 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 3-D point cloud acquisition system is proposed. First, a space sphere with an unknown radius is chosen as the calibration target. Second, 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. Third, combining the success-history-based parameter adaptation for differential evolution (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 3-D 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.