A Lightweight Method for Detecting Dynamic Target Occlusions by the Robot Body

Autonomous robots face significant challenges in accurately perceiving their environment due to occlusions. The robot itself may hide targets of interest from the camera while moving within the field of view, which can lead to safety hazards or failures in task execution. For example, if a target of interest is partially occluded by the robot, detecting and grasping it correctly, becomes very challenging. To solve this problem, we propose a novel computationally lightweight method to determine the areas that the robot occludes. Our approach uses the Unified Robot Description Format (URDF) to generate a virtual depth image of the 3D robot model. With the virtual depth image we can effectively determine the partially occluded areas to improve the robustness of the information given by the perception system. Due to the real-time capabilities of the method, it can successfully detect occlusions of moving targets by the moving robot. We validate the effectiveness of the method in an experimental setup using a 6-DoF robot arm and an RGB-D camera by detecting and handling occlusions for two tasks: Pose estimation of a moving object for pickup and human tracking for robot handover. The code is available in https://github.com/auth-arl/virtual_depth_image .