Pose Estimation of 3D Objects Based on Point Pair Feature and Weighted Voting

Abstract

Abstract3D object pose estimation is an important part of machine vision and robot grasping technology. In order to further improve the accuracy of 3D pose estimation, we propose a novel method based on the point pair feature and weighted voting (WVPPF). Firstly, according to the angle characteristics of the point pair features, the corresponding weight is added to the vote of point pair matching results, and several initial poses are obtained. Then, we exploit initial pose verification to calculate the coincidence between the model and the scene point clouds after the initial pose transformation. Finally, the pose with the highest coincidence is selected as the result. The experiments show that WVPPF can estimate pose effectively for a 60%–70% occlusion rate, and the average accuracy is 17.84% higher than the point pair feature algorithm. At the same time, the WVPPF has good applicability in self-collected environments.Keyword3D objectPoint pair featureWeighted votingPose verificationPose estimation