Abstract
Visual object grasping can be complex when dealing with different shapes, points of view, and environments since the robotic manipulator must estimate the most feasible place to grasp. This work proposes a new selective grasping system using only point clouds of objects. For the selection of the object of interest, a deep learning network for object classification is proposed, named Point Encoder Convolution (PEC). The network is trained with a dataset obtained in a realistic simulator and uses an autoencoder with 1D convolution. The developed grasping algorithm used in the system uses geometry primitives and lateral curvatures to estimate the best region to grasp without previously knowing the object’s point cloud. Experimental results show a success ratio of 94% for a dataset with five classes, and the proposed visual selective grasping system can be executed in around 0.004 s, suitable for tasks that require a low execution time or use low-cost hardware.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
