Prior Information-Assisted Neural Network for Point Cloud Segmentation in Human-Robot Interaction Scenarios

Abstract

This letter proposes a prior information-assisted (PIA) point cloud segmentation network that can be effectively applied to point cloud segmentation applications in human-robot interaction scenario. The joint angles of the robots are used as prior information, which is fed into the network as an additional input in the form of a vector along with the actual point cloud of the target scene. The PIA network incorporates a point cloud generation network and a simulation-assisted segmentation network. Based on the joint angles of the robots, the generation network generates a point cloud for a simulated scene without people or obstacles. Several new loss functions are proposed to train the point cloud generation network. The simulation-assisted segmentation network extracts and compares the features of the actual point cloud with those of the simulated point cloud and segments the actual point cloud. Experiments conducted on a homemade point cloud dataset involving an industrial scene demonstrate that the proposed approach can achieve significantly improved segmentation performance and network robustness.