Vision-based Robotic Grasp Detection From Object Localization, Object Pose Estimation To Grasp Estimation: A Review [article]

Guoguang Du, Kai Wang, Shiguo Lian, Kaiyong Zhao
2020 arXiv   pre-print
This paper presents a comprehensive survey on vision-based robotic grasp detection methods. We concluded three key tasks during robotic grasping, which are object localization, object pose estimation and grasp estimation. In detail, object localization task contains object localization without classification, object detection and object instance segmentation. This task provides the regions of the target object in the input data. Object pose estimation mainly refers to estimating the 6D object
more » ... se and includes correspondence-based methods, template-based methods and voting-based methods, which affords the generation of grasp poses. Grasp estimation includes 2D planar grasp methods and 6DoF grasp methods, where the former is constrained to grasp from one direction. All the above subtasks are reviewed with traditional methods and latest deep learning-based methods based on the RGB-D image inputs. These three subtasks could accomplish the robotic grasping task with different combinations. Some object pose estimation methods need not object localization, and they conduct object localization and object pose estimation jointly. Some grasp estimation methods need not object localization and object pose estimation, and they conduct grasp estimation in an end-to-end manner. These methods are reviewed elaborately in this survey and related datasets and comparisons between state-of-the-art methods are summarized. In addition, challenges about vision-based robotic grasping, and future directions in addressing these challenges are also pointed out.
arXiv:1905.06658v2 fatcat:6u3k2ltwifaanjpp2nkayyj2f4