Robust rigid point cloud registration is effective for accurate positioning and measurement of complex components. The existing registration algorithms, however, fail to overcome the matching distortion caused by structural deviation, unknown abnormal allowance, and various measurement inherent defects. Although the recently proposed VMM and WPMAVM algorithms can inhibit the matching distortion to some extent, they still fail in the presence of numerous abnormal points. In this study, we present a progressive and adaptive variance minimization (PAVM) algorithm to address these issues. A progressive de-pseudo weight is established to ensure the involvement of all point pairs in optimization at the initial registration stage. Then, an approximately truncated weight function is employed to mitigate the influence of abnormal points on registration results. Furthermore, a novel adaptive coordination distance function is established by improving the symmetric point-to-plane distance metric and combining the first-order approximate point-to-point distance metric, which enhances the algorithm speed and stability. The analysis investigates the anti-abnormal interference ability and quadratic convergence, validating the feasibility of the PAVM algorithm. Experiments are undertaken to illustrate the notable benefits of our algorithm in convergence stability, matching speed, and universality. These attributes render the algorithm well-suited for registration tasks involving diverse complex components.

Robotic grinding is utilized for machining large complex components due to its exceptional flexibility and expansive workspace. However, the existing research primarily focuses on the global machining and lacks solutions for repair and remanufacturing of components with local defects. In order to perform the local repair efficiently, this paper proposes a 3D vision-guided robotic grinding framework for repairing random defects, by taking the removal of welding slags on the automotive body as an example. Based on the measured point cloud, a robust function weighted variance minimization (RFWVM) registration algorithm is utilized to position the automotive body with high precision. Meanwhile, the datum plane is reconstructed to extract the defect point clouds. On this basis, the welding slags are divided into regions in accordance with their height characteristics. For these regions, a robotic grinding depth prediction model is then constructed to determine the mapping between welding slag height and process parameters, and the genetic algorithm is further improved to solve the shortest path decision-making problem in robotic grinding. The experimental results conducted in a typical region of an automotive body confirm the practicality and effectiveness of the proposed framework. This study provides a valuable reference for local defects repair of complex components.