Bin Picking Approaches Based on Deep Learning Techniques: A State-of-the-Art Survey

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Cordeiro, A (Author) Other; Rocha, LF (Author) FEUP; Costa, C (Author) Other; Pedro Luís Cerqueira Gomes da Costa (Author) FEUP; Silva, MF (Author) Other

Conference proceedings International

Title: IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2022, Santa Maria da Feira, Portugal, April 29-30, 2022

Pages: 110-117

Indexing

ISI Web of Knowledge - 14 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00W-SHC

DOI: 10.1109/icarsc55462.2022.9784795

Abstract (EN): Bin picking is a highly researched topic, due to the need for automated procedures in industrial environments. A general bin picking system requires a highly structured process, starting with data acquisition, and ending with pose estimation and grasping. A high number of bin picking problems are being presently solved, through deep learning networks, combined with distinct procedures. This study provides a comprehensive review of deep learning approaches, implemented in bin picking problems. Throughout the review are described several approaches and learning methods based on specific domains, such as gripper oriented and object oriented, as well as summarized several methodologies, in order to solve bin picking issues. Furthermore, are introduced current strategies used to simplify particular cases and at last, are presented peculiar means of detecting object poses.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8