Abstract
This paper presents a novel 3D feature descriptor for object recognition and to identify poses when there are six-degrees-of-freedom for mobile manipulation and grasping applications. Firstly, a Microsoft Kinect sensor is used to capture 3D point cloud data. A viewpoint feature histogram (VFH) descriptor for the 3D point cloud data then encodes the geometry and viewpoint, so an object can be simultaneously recognized and registered in a stable pose and the information is stored in a database. The VFH is robust to a large degree of surface noise and missing depth information so it is reliable for stereo data. However, the pose estimation for an object fails when the object is placed symmetrically to the viewpoint. To overcome this problem, this study proposes a modified viewpoint feature histogram (MVFH) descriptor that consists of two parts: a surface shape component that comprises an extended fast point feature histogram and an extended viewpoint direction component. The MVFH descriptor characterizes an object’s pose and enhances the system’s ability to identify objects with mirrored poses. Finally, the refined pose is further estimated using an iterative closest point when the object has been recognized and the pose roughly estimated by the MVFH descriptor and it has been registered on a database. The estimation results demonstrate that the MVFH feature descriptor allows more accurate pose estimation. The experiments also show that the proposed method can be applied in vision-guided robotic grasping systems.
Highlights
Robotic grasping systems cannot quickly or accurately recognize randomly oriented objects that exit an assembly line or which are located on an assembly table so machine vision is used to solve this problem
3D3D feature descriptor for object recognition and identifying the pose in mobile manipulation based object recognition still imposes additional challenges related to scaling, viewpoint variation, and grasping applications where there are six-degrees-of-freedom descriptor is robust partial occlusions, and background clutter
Object pose estimation when objects are symmetrically placed withdescriptor relation is to the robust against a large degree of surface noise and missing depth information so it is reliable for stereo viewpoint
Summary
Robotic grasping systems cannot quickly or accurately recognize randomly oriented objects that exit an assembly line or which are located on an assembly table so machine vision is used to solve this problem. 23D-based of 14 cameras, the features object recognition still imposes additional challenges related to scaling, viewpoint variation, partial by perspective projection in 2D vision. 3D3D feature descriptor for object recognition and identifying the pose in mobile manipulation based object recognition still imposes additional challenges related to scaling, viewpoint variation, and grasping applications where there are six-degrees-of-freedom (6-DOF). Object pose estimation when objects are symmetrically placed withdescriptor relation is to the robust against a large degree of surface noise and missing depth information so it is reliable for stereo viewpoint. To overcome this problem, a modified viewpoint feature histogram (MVFH) descriptor is data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have