文章基本信息

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 等
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.