6D pose estimation of object based on fused region-level feature in cluttered scenes

Abstract

RGB-based methods have certain disadvantages because they lack 3D information and cannot cope with the pose estimation problem in cluttered scenes. Therefore, we propose a region-level pose estimation network that uses RGB-D images. We first extracted the objects’ color features and geometric features by convolutional neural networks (CNN) and PointNet, respectively, and then performed feature fusion. The fused features were fed into a region-level feature extraction network to obtain the region-level features, which extracted the local geometry features from the point cloud and learned the point set’s semantic information. We used the output of the region-level feature extraction network to perform region-level pose estimation, then selected the pose with the highest confidence level as the output and iteratively optimized the pose to obtain the final results. The experimental results showed that the proposed solution performed well on the LINEMOD data set, which verified the effectiveness of the proposed method in the pose estimation problem and the algorithm’s robustness in severely cluttered scenes.