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This publication is part of the R&D project "Cognitive Personal Assistance for Social Environments (ACOGES)", reference PID2020-113096RB-I00, funded by MCIN/AEI/10.13039/501100011033. We would also like to thank M. Beteta and F. Rodriguez for previous contributions to this work.

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AMCL hybrid localization through the incorporation of visual rectangular landmarks

Publicado en:2024 28th International Conference On Methods And Models In Automation And Robotics, Mmar 2024. 556-561 - 2024-01-01 (), DOI: 10.1109/MMAR62187.2024.10680758

Autores: Vega-Martínez, G; Moratalla, JL; de la Puente, P

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Resumen

The increasing integration of robots into daily life requires enhanced autonomy capabilities and better levels of robustness. To address robot localization, this work proposes Hybrid AMCL, an extension of the original Adaptive Monte Carlo Localization (AMCL) algorithm, incorporating visual cues in the form of natural rectangles, usually present indoors, to overcome difficulties posed by scenarios lacking geometric features such as corridors. To this end, this work also introduces Rectangle Intersection-based Detector using Graphs and Elongation (RIDGE), a novel algorithm based on the idea of finding quadrilaterals in images, corresponding to real-world rectangles. The effectiveness of Hybrid AMCL is validated both in simulation using Isaac Sim and in a real-world environment. Our results show its potential for more accurate localization in challenging situations.

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