现有的三维物体识别和位姿估计方法无法很好地用于散乱堆放物体的场景,尤其是有严重遮挡和混叠的场景。使用基于点对特征的点云匹配和位姿估计算法,针对工业环境中乱序物体的特点,进行了一系列改进,如场景点云法线方向一致性调整、抓取位姿筛选策略调整、旋转对称引起的角度偏差调整,以取得更理想的位姿估计结果。在仿真环境和真实场景下进行了一系列实验,实验结果表明,所采用的算法在乱序物体场景中的位姿估计效果比较理想。

The existing three-dimensional object recognition and pose estimation methods cannot solve the scene of random bins well, especially for scenes with severe occlusion and clutter. Aiming at this problem, a point cloud matching and pose estimation algorithm based on point pair features is used in this paper. A series of improvements are made to obtain more ideal pose estimation results according to the characteristics of random bins in industrial environments, such as the adjustment of the normal direction consistency of the scene point clouds, the adjustment of the grab pose filtering strategy, and the adjustment of angular deviation caused by the rotation symmetry. In this paper, a series of experiments are carried out in the simulation environment and the real environment. Experimental results show that the adopted algorithm has good pose estimation effect in the scene of random bins.