A Calibration Algorithm of 3-D Laser Scanner Based on KMPE Loss Function

Aiming at solving the estimation problem of measurement model parameters of a 3-D laser scanner, a kernel mean ${p}$ -power error (KMPE) loss function-based calibration algorithm is proposed in this article. The KMPE loss function is robust to measurement noise and outliers. Thus, a KMPE loss function-based nonlinear optimization model of measurement model parameters of a 3-D laser scanner is first established according to the distance constraint between each laser scanning point on the calibration sphere and the center of the calibration sphere. Then, to conduct calibration of the model parameters, the optimization model is solved by combining the success-history-based parameter adaptation for differential evolution (SHADE) algorithm with the Levenberg-Marquardt (LM) algorithm. Experimental results show that the proposed algorithm can achieve high calibration accuracy of the measurement model parameters of the 3-D laser scanner by effectively suppressing the influence of measurement noise and outliers.