A Fast Global Method Combined with Local Features for 6D Object Pose Estimation

Abstract

With the advancement of intelligent manufacturing, robots are increasingly involved in industrial production, for example, replacing human beings to identify and manipulate objects. However, it is still a challenging task for object recognition and pose estimation in industrial environment, especially under large environmental noise, mutual occlusions and high-speed requirements. In this paper, we propose a new method for 6D object pose estimation, Super Key 4-Points Congruent Sets (SK-4PCS), which is a fast global method combined with local features. The proposed method includes three steps. Firstly, the key points in 3D point clouds are extracted by local features, which are invariant with respect to rotation and translation. Secondly, the smart indexing scheme is used to realize the query of 4-points congruent sets globally. Finally, the coarse pose obtained quickly is further refined by the Iteration Closest Point algorithm (ICP) [1]. The proposed method is also compared with existing methods by the evaluation indicator Visible Surface Discrepancy (VSD) on the LINEMOD dataset [2]. The experimental results show that the refined SK-4PCS results in significant speedup and satisfactory accuracy in 6D object pose estimation.