Robot vision is greatly affected by occlusions, which poses challenges to autonomous systems. The robot itself may hide targets of interest from the camera, while it moves within the field of view, leading to 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 computationally lightweight method to determine the areas that the robot occludes. For this purpose, we use the Unified Robot Description Format (URDF) to generate a virtual depth image of the 3D robot model. Using 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 \url{https://github.com/auth-arl/virtual\_depth\_image}.

翻译：机器人视觉受到遮挡的严重影响，这给自主系统带来了挑战。机器人本体的运动可能导致其自身在相机视野中遮挡感兴趣目标，从而造成任务执行失败。例如，当感兴趣目标被机器人部分遮挡时，准确检测并抓取该目标将变得非常困难。为解决此问题，我们提出了一种计算轻量级的方法，用于确定机器人遮挡的区域。为此，我们使用统一机器人描述格式（URDF）生成机器人三维模型的虚拟深度图像。通过虚拟深度图像，我们可以有效确定部分遮挡区域，从而提升感知系统所提供信息的鲁棒性。得益于该方法的实时性，它能成功检测运动机器人对动态目标的遮挡。我们通过实验验证了该方法的有效性：采用六自由度机械臂与RGB-D相机，在目标拾取的位姿估计和机器人交接的人体跟踪两项任务中检测并处理遮挡。代码开源地址为 \url{https://github.com/auth-arl/virtual\_depth\_image}。