Learning-Based Underwater Autonomous Grasping via 3D Point Cloud

Cong Wang, Qifeng Zhang, Shuo Li, Xiaohui Wang, David Lane, Yvan Petillot, Sen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Underwater autonomous grasping is a challenging task for robotic research. In this paper, we propose a learning-based underwater grasping method using 3D point cloud generated from an underwater stereo camera. First, we use Pinax-model for accurate refraction correction of a stereo camera in a flat-pane housing. Second, dense point cloud of the target is generated using the calibrated stereo images. An improved Grasp Pose Detection (GPD) method is then developed to generate the candidate grasping poses and select the best one based on kinematic constraints. Finally, an optimal trajectory is planned to finish the grasping task. Experiments in a water tank have proved the effectiveness of our method.

Original languageEnglish
Title of host publicationOCEANS 2021
Subtitle of host publicationSan Diego - Porto
PublisherIEEE
ISBN (Electronic)9780692935590
DOIs
Publication statusPublished - 15 Feb 2022
EventOCEANS 2021: San Diego - Porto - San Diego, United States
Duration: 20 Sep 202123 Sep 2021

Conference

ConferenceOCEANS 2021: San Diego - Porto
Country/TerritoryUnited States
CitySan Diego
Period20/09/2123/09/21

Keywords

  • 3D point cloud
  • Grasp Pose Detection (GPD)
  • Stereo camera
  • Underwater grasping

ASJC Scopus subject areas

  • Oceanography

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