Using Simulation to Evaluate a Tube Perception Algorithm for Bin Picking

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Leao, G (Author) Other; Costa, CM (Author) Other; Armando Jorge Sousa (Author) FEUP; reis, lp (Author) FEUP; Germano Veiga (Author) FEUP

Journal

Title: Robotics

Vol. 11

Final page: 46

Publisher: MDPI

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00W-C07

DOI: 10.3390/robotics11020046

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14