A two-stage 6D pose estimation method for industrial textureless parts based on multi-feature fusion

Abstract 6D pose estimation is an essential supporting technology for many industrial applications such as robotic vision, human-robot collaboration and augmented reality. However, in industrial environments, due to the textureless, reflective, and occluded characteristics of industrial parts, the accuracy and adaptability to the application environment of pose estimation are limited. To solve this issue, a two-stage 6D pose estimation method for industrial parts is proposed, which uses a multi-feature fusion strategy. In the first stage, the semantic keypoints are selected to train a PVN3D-based RGBD fusion pose estimation network to predict the initial pose. In the second stage, we propose a pose iterative optimization method based on the fusion of appearance and geometric features. Experiments on the MP6D industrial dataset demonstrate that the proposed method exhibits the comparative methods. Our approach offers a novel idea for accurate and robust pose estimation of industrial parts.