A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Klein, LC (Author) Other; Braun, J (Author) Other; Mendes, J (Author) Other; Pinto, VH (Author) FEUP; Martins, FN (Author) Other; de Oliveira, AS (Author) Other; Wortche, H (Author) Other; Paulo Gomes da Costa (Author) FEUP; Lima, J (Author) Other

Journal

Title: Sensors

Vol. 23

Final page: 3128

ISSN: 1424-3210

Publisher: MDPI

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00Y-1N7

DOI: 10.3390/s23063128

Abstract (EN): Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exact positions of the fiducial markers, as in the analytical approach.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17