C-LFNet: Central-Local Feature 3D point cloud instance segmentation Network for robot bin-picking

Abstract

We propose a novel instance segmentation algorithm called Central-Local Feature Network (C-LFNet) to address the low performance of point cloud segmentation in bin-picking. First, to address the issue of batch processing data, we propose a downsampling algorithm that can sample the number of target points while preserving the density distribution of points in space as much as possible both before and after sampling. Second, we design a dynamic graph convolution backbone to better extract the local geometric invariant features, referred to as Central-Local Feature Module (C-LFM). Finally, the results of each point estimate are clustered in order to enable instance segmentation of the point cloud. The proposed network is evaluated using the Fraunhofer IPA Bin-Picking dataset and compared to existing methods. Results demonstrate that the proposed network is effective and robust in bin-picking scenes to achieve the same category of point cloud instance segmentation, resulting in improved performance.