3D Reconstruction of End-Effector in Autonomous Positioning Process Using Depth Imaging Device

Abstract

The real-time calculation of positioning error, error correction, and state analysis has always been a difficult challenge in the process of manipulator autonomous positioning. In order to solve this problem, a simple depth imaging equipment (Kinect) is used and Kalman filtering method based on three-frame subtraction to capture the end-effector motion is proposed in this paper. Moreover, backpropagation (BP) neural network is adopted to recognize the target. At the same time, batch point cloud model is proposed in accordance with depth video stream to calculate the space coordinates of the end-effector and the target. Then, a 3D surface is fitted by using the radial basis function (RBF) and the morphology. The experiments have demonstrated that the end-effector positioning error can be corrected in a short time. The prediction accuracies of both position and velocity have reached 99% and recognition rate of 99.8% has been achieved for cylindrical object. Furthermore, the gradual convergence of the end-effector center (EEC) to the target center (TC) shows that the autonomous positioning is successful. Simultaneously, 3D reconstruction is also completed to analyze the positioning state. Hence, the proposed algorithm in this paper is competent for autonomous positioning of manipulator. The algorithm effectiveness is also validated by 3D reconstruction. The computational ability is increased and system efficiency is greatly improved.