关键词: 点云配准, 异常点云干扰, 鲁棒函数, 车身构件, 机器人视觉测量

Abstract: Point cloud registration was a key method for pose parameter measurement of large vehicle body components, but the existing algorithms were difficult to register to effective pose under a large number of abnormal point cloud interference, thereby resulting in matching distortion and inability to ensure the quality of subsequent robotic operations. To address the issue, a robust registration algorithm for vehicle body components, robust function weighted variance minimization(RFWVM) algorithm was proposed that might effectively suppress the interference of abnormal point cloud. A robust function weighted objective function was established, and the influences of abnormal point cloud in the registration processes were suppressed by applying dynamic weights that varied with the number of iterations. The rigid transformation matrix was solved iteratively by the Gauss-Newton method. The experimental results on the side walls of high-speed rail body and car door frames demonstrate that the proposed RFWVM algorithm has higher registration accuracy compared to classic algorithms, such as interactive closure point(ICP), variance minimization(VMM), weighted plus and minimum allowance variance minimization(WPMAVM), de-pseudo-weighted variance minimization(DPWVM), may effectively suppress the influences of various abnormal point clouds on registration results, and also behaves better stability and robustness. The method may effectively achieve the accurate registration of various vehicle body components.

Key words: point cloud registration, abnormal point cloud interference, robust function, vehicle body component, robotic vision measurement