Robotic Manipulation Based on 3D Vision: A Survey

Abstract

Grasping has long been studied in the field of robotics. In this paper, we divide the process of robotic grasp into sensing and control. In terms of sensing, 2D vision based sensing relies on accurate feature matching and object surface texture features, resulting in poor performance in the complex environment with occlusion. By contrast, some sensors based on 3D vision are more robust to noise. Processing point clouds in a deep learning method can achieve high accuracy as well as reducing the computation time compared with those using cost volume regularization. For the control part, the traditional trajectory motion methods are limited to generalization and grasping with high degrees of freedom. On the contrary, the methods of reinforcement learning can improve the grasping strategy in the continuous interaction with the environment. We propose some commonly used benchmarks and simulation platforms for simulation experiment using reinforcement learning.