Abstract: In complex component assembly scenarios based on augmented reality assembly guidance,the occlusion of the parts by the hands leads to significant errors and even failure in the pose calculation of the parts. At present,pose estimation algorithms for manually assembled parts do not consider the use of hand information when solving part occlusion problems,making it difficult for pose estimation accuracy to meet the requirements of augmented assembly. In response to the above issues,the article proposes a 6D pose estimation algorithm for parts that integrates hand posture,namely the HandICG algorithm. This algorithm integrates the pose information of the hand with the Iterative Corresponding Geometry (ICG) algorithm. When hand occlusion occurs,the pose information of the hand is applied to the solution of part pose,significantly improving the accuracy of object pose estimation under hand occlusion. Experiments show that the Average Distance of Model points (ADD) index reaches 74.73 %,which is 2.61 times that of ICG. This algorithm significantly improves the accuracy and robustness of part pose calculation in augmented assembly scenarios.

Key words: pose estimation, hand pose, occlusion, augmented reality, tracking and registration