Robust Few-Shot Pose Estimation of Articulated Robots using Monocular Cameras and Deep-Learning-based Keypoint Detection

Camera-based pose estimation is a necessity for flexible applications in robotics, especially interaction between robots and mobile entities. Inspired by recent advancements in human pose estimation based on Convolutional Neural Networks, it is aspired to substitute the usage of artificial marker by automatically detecting inherent keypoints of the robot representing its 2D skeleton model. In addition, current encoder readings of the robot are utilized establishing the corresponding 3D skeleton model through forward kinematics. With the help of these 2D - 3D point correspondences, an estimation of the translation and orientation deviation between robot and camera is derived solving the perspective-n-point problem. An adequate approach for markerless keypoint detection of an UR5 robot is presented and evaluated in terms of precision and pose dispersion considering a dynamically moving robot. The promising results show that the novel method works robustly and reliably as a few-shot approach and copes with false positives as well as with partly occlusions and non-detected keypoints. Further potential is identified regarding enhancing the accuracy through the use of synthetic data.