IPPE-PCR: a novel 6D pose estimation method based on point cloud repair for texture-less and occluded industrial parts

Fast and accurate 6D pose estimation can help a robot arm grab industrial parts efficiently. The previous 6D pose estimation algorithms mostly target common items in daily life. Few algorithms are aimed at texture-less and occluded industrial parts and there are few industrial parts datasets. A novel method called the Industrial Parts 6D Pose Estimation framework based on point cloud repair (IPPE-PCR) is proposed in this paper. A synthetic dataset of industrial parts (SD-IP) is established as the training set for IPPE-PCR and an annotated real-world, low-texture and occluded dataset of industrial parts (LTO-IP) is constructed as the test set for IPPE. To improve the estimation accuracy, a new loss function is used for the point cloud repair network and an improved ICP method is proposed to optimize template matching. The experiment result shows that IPPE-PCR performs better than the state-of-the-art algorithms on LTO-IP.