  This paper presents a comprehensive survey on vision-based robotic grasping.
We conclude three key tasks during vision-based robotic grasping, which are
object localization, object pose estimation and grasp estimation. In detail,
the 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. The object pose
estimation task mainly refers to estimating the 6D object pose and includes
correspondence-based methods, template-based methods and voting-based methods,
which affords the generation of grasp poses for known objects. The grasp
estimation task includes 2D planar grasp methods and 6DoF grasp methods, where
the former is constrained to grasp from one direction. These three tasks could
accomplish the robotic grasping with different combinations. Lots of object
pose estimation methods need not object localization, and they conduct object
localization and object pose estimation jointly. Lots of grasp estimation
methods need not object localization and object pose estimation, and they
conduct grasp estimation in an end-to-end manner. Both traditional methods and
latest deep learning-based methods based on the RGB-D image inputs are reviewed
elaborately in this survey. Related datasets and comparisons between
state-of-the-art methods are summarized as well. In addition, challenges about
vision-based robotic grasping and future directions in addressing these
challenges are also pointed out.
