Binocular laser scanning imaging-based disordered object recognition method.

This paper takes industrial robot disordered object grasping as its research background and proposes an object recognition method based on binocular laser three-dimensional (3D) scanning imaging for the characteristics of grasping objects with complex structures, single surface colors, and scattered stacking. First, to address the issue of incomplete data in the 3D reconstruction of stacked scenes caused by self-obscuration and mutual occlusion of objects, this paper proposes a binocular laser scanning 3D imaging method that utilizes a rapid fusion of multi-source point cloud data. Secondly, this paper reports improved point cloud matching of a pose estimation algorithm based on the point pair feature (PPF) to achieve accurate pose estimation of scattered stacked objects. In the offline training stage, the hidden point removal (HPR) method is introduced to impose visibility constraints, eliminating interference from occluded points and achieving a more accurate global description of the model. In the online matching stage, farthest point sampling (FPS) is applied to the scene point cloud to improve the uniformity of the sampling results. Through Hough voting, candidate poses are quickly obtained. The rough estimation of the object pose was completed by adjusting the grasping pose screening strategy to remove the mismatched poses. Finally, the iterative closest point (ICP) algorithm is used to optimize the poses and obtain more accurate pose results. The experimental results demonstrate that the object information reconstruction completeness of the proposed method is significantly improved compared with the original binocular vision and monocular laser vision system, and the recognition time and average error are reduced compared to the original PPF algorithm. Furthermore, the matching success rate is 100% in 20 cluttered scenes. In the actual grasping experiments, the success rate of grasping is 93.3%, which verifies the proposed method's effectiveness for cluttered occlusion.