Research on Key Point Detection Method of Mechanical Arm

Abstract

Accurate keypoint localization is an important research direction in the field of robotic arms. Deep learning is being used more and more for motion capture and pose estimation. This is due to their ability to detect user-defined key points without the use of labels. We present an approach for detecting robot 3D key points coordinate based on two perspectives. Two images of different perspectives are processed by the same deep neural network to detect 2D projections of key points (such as joints) associated with the robot. Triangularization is then applied to recover 3D coordinates. In addition, the existing dataset of robot critical points is obtained by simulation. It seriously impedes the advancement of this field. We establish an actual robot key points dataset and corresponding evaluation criterion. The experimental results demonstrate that our framework achieves cutting-edge detection performance. It is reusable for other robot models.