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  • 标题:Using Simulation to Evaluate a Tube Perception Algorithm for Bin Picking
  • 本地全文:下载
  • 作者:Gonçalo Leão ; Carlos M. Costa ; Armando Sousa
  • 期刊名称:Robotics
  • 电子版ISSN:2218-6581
  • 出版年度:2022
  • 卷号:11
  • 期号:2
  • 页码:46
  • DOI:10.3390/robotics11020046
  • 语种:English
  • 出版社:MDPI Publishing
  • 摘要:Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. In order to provide ground truth data for evaluating heuristic or machine learning perception systems, this paper proposes using simulation to create bin picking environments in which a procedural generation method builds entangled tubes that can have curvatures throughout their length. The output of the simulation is an annotated point cloud, generated by a virtual 3D depth camera, in which the tubes are assigned with unique colors. A general metric based on micro-recall is proposed to compare the accuracy of point cloud annotations with the ground truth. The synthetic data is representative of a high quality 3D scanner, given that the performance of a tube modeling system when given 640 simulated point clouds was similar to the results achieved with real sensor data. Therefore, simulation is a promising technique for the automated evaluation of solutions for bin picking tasks.
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