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Abstract
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.
Original language | English |
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Article number | 3128 |
Journal | Sensors |
Volume | 23 |
Issue number | 6 |
DOIs | |
Publication status | Published - 15 Mar 2023 |
Keywords
- fiducial markers
- indoor localization
- industry 4.0
- machine learning
- robotics competitions
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Dive into the research topics of 'A Machine Learning Approach to Robot Localization Using Fiducial Markers in RobotAtFactory 4.0 Competition'. Together they form a unique fingerprint.Activities
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Hanze Impact Event
Heinrich Wörtche (Speaker) & Frank Manting (Speaker)
31 May 2023Activity: Participating in or organising an event › Participation in workshop, seminar, course