論文

2021年5月

Robust grasp detection with incomplete point cloud and complex background

ADVANCED ROBOTICS
  • Xixun Wang
  • ,
  • Sajid Nisar
  • ,
  • Fumitoshi Matsuno

35
10
開始ページ
619
終了ページ
634
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1080/01691864.2021.1897674
出版者・発行元
TAYLOR & FRANCIS LTD

Point cloud information is a convenient means to accomplish grasp detection in autonomous robotic grasping. However, the performance of various state-of-the-art grasp detection methods that use point cloud decreases significantly when the available point cloud information is incomplete and the background is complex, i.e. heterogeneous. To solve this problem, we propose a robust grasp detection method that demonstrates higher performance, especially with incomplete point clouds and complex backgrounds. We introduce a novel technique named 'visible point-cloud' - generated using the point cloud and pose (position and orientation) information of the sensor(s) - that helps to eliminate unsafe grasp candidates quickly and efficiently. The remaining grasp candidates are then classified and the best candidate is determined using a cost function. The effectiveness of the proposed method is shown experimentally using a 6-DoF robot arm equipped with a two-finger gripper with three different background settings: (a) steps, (b) pillars, and (c) table top. The results show that the proposed method is 1.20 times faster and has a 20% higher grasp success rate than a state-of-the-art method for a single point cloud camera. The results demonstrate that the proposed method significantly improves the performance of autonomous grasping even with incomplete point cloud and is robust for different backgrounds.

リンク情報
DOI
https://doi.org/10.1080/01691864.2021.1897674
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000627253300001&DestApp=WOS_CPL
ID情報
  • DOI : 10.1080/01691864.2021.1897674
  • ISSN : 0169-1864
  • eISSN : 1568-5535
  • Web of Science ID : WOS:000627253300001

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