Point Cloud Projective Analysis for Part-Based Grasp Planning
This work presents an approach for part-based grasp planning in point clouds. A complete pipeline is proposed that allows a robot manipulator equipped with a range camera to perform object detection, categorization, segmentation into meaningful parts, and part-based semantic grasping. A supervised i...
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| Published in: | IEEE robotics and automation letters Vol. 5; no. 3; pp. 4695 - 4702 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Piscataway
IEEE
01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
| Online Access: | Get full text |
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| Abstract | This work presents an approach for part-based grasp planning in point clouds. A complete pipeline is proposed that allows a robot manipulator equipped with a range camera to perform object detection, categorization, segmentation into meaningful parts, and part-based semantic grasping. A supervised image-space technique is adopted for point cloud segmentation based on projective analysis. Projective analysis generates a set of 2D projections from the input object point cloud, labels each object projection by transferring knowledge from existing labeled images, and then fuses the labels by back-projection on the object point cloud. We introduce an algorithm for point cloud categorization based on 2D projections. We also propose a viewpoint aware algorithm that filters 2D projections according to the scanning path of the robot. Object categorization and segmentation experiments were carried out with both synthetic and real datasets. Results indicate that the proposed approach performs better than a CNN-based method for a training set of limited size. Finally, we show part-based grasping tasks in a real robotic setup. |
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| AbstractList | This work presents an approach for part-based grasp planning in point clouds. A complete pipeline is proposed that allows a robot manipulator equipped with a range camera to perform object detection, categorization, segmentation into meaningful parts, and part-based semantic grasping. A supervised image-space technique is adopted for point cloud segmentation based on projective analysis. Projective analysis generates a set of 2D projections from the input object point cloud, labels each object projection by transferring knowledge from existing labeled images, and then fuses the labels by back-projection on the object point cloud. We introduce an algorithm for point cloud categorization based on 2D projections. We also propose a viewpoint aware algorithm that filters 2D projections according to the scanning path of the robot. Object categorization and segmentation experiments were carried out with both synthetic and real datasets. Results indicate that the proposed approach performs better than a CNN-based method for a training set of limited size. Finally, we show part-based grasping tasks in a real robotic setup. |
| Author | Monica, Riccardo Aleotti, Jacopo |
| Author_xml | – sequence: 1 givenname: Riccardo orcidid: 0000-0002-1262-6348 surname: Monica fullname: Monica, Riccardo email: riccardo.monica@unipr.it organization: Department of Engineering and Architecture, RIMLab, Robotics and Intelligent Machines Laboratory, University of Parma, Parma, Italy – sequence: 2 givenname: Jacopo orcidid: 0000-0003-2498-932X surname: Aleotti fullname: Aleotti, Jacopo email: jacopo.aleotti@unipr.it organization: Department of Engineering and Architecture, RIMLab, Robotics and Intelligent Machines Laboratory, University of Parma, Parma, Italy |
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| SubjectTerms | Algorithms Cameras Classification Grasping Grasping (robotics) Image segmentation Labels object detection Object recognition Perception for grasping and manipulation Planning Robot arms Robots segmentation and categorization semantic scene understanding Three-dimensional displays Two dimensional analysis Two dimensional displays |
| Title | Point Cloud Projective Analysis for Part-Based Grasp Planning |
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