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Abstract
Autonomous navigation is one of the main areas of research in mobile robots and intelligent connected vehicles. In this context, we are interested in presenting a general view on robotics, the progress of research, and advanced methods related to this field to improve autonomous robots’ localization. We seek to evaluate algorithms and techniques that give robots the ability to move safely and autonomously in a complex and dynamic environment. Under these constraints, we focused our work in the paper on a specific problem: to evaluate a simple, fast and light SLAM algorithm that can minimize localization errors. We presented and validated a FastSLAM 2.0 system combining scan matching and loop closure detection. To allow the robot to perceive the environment and detect objects, we have studied one of the best deep learning technique using convolutional neural networks (CNN). We validate our testing using the YOLOv3 algorithm.
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Authors and Affiliations
University of Quebec in Chicoutimi, UQAC, 555 bvd de l’Université, Chicoutimi, Québec, G7H 2B1, Canada
Abdellah Chehri & Ahmed Zarai
Faculty of Informatics, Reutlingen University, Germany Reutlingen University, Alteburgstraße 150, 72762, Reutlingen, Germany
Alfred Zimmermann
SIRC/LaGeS-EHTP, EHTP Km 7 Route El Jadida, Oasis, Morocco
Rachid Saadane
- Abdellah Chehri
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- Ahmed Zarai
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- Alfred Zimmermann
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- Rachid Saadane
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Correspondence toAbdellah Chehri.
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Editors and Affiliations
Reutlingen University, Reutlingen, Germany
Alfred Zimmermann
'Aurel Vlaicu' University of Arad, Romania, Bournemouth University & KES International Research, Shoreham-by-Sea, UK
Robert J. Howlett
KES International, Shoreham-by-Sea, UK
Lakhmi C. Jain
Munich University of Applied Sciences, Munich, Germany
Rainer Schmidt
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Chehri, A., Zarai, A., Zimmermann, A., Saadane, R. (2021). 2D Autonomous Robot Localization Using Fast SLAM 2.0 and YOLO in Long Corridors. In: Zimmermann, A., Howlett, R.J., Jain, L.C., Schmidt, R. (eds) Human Centred Intelligent Systems . KES-HCIS 2021. Smart Innovation, Systems and Technologies, vol 244. Springer, Singapore. https://doi.org/10.1007/978-981-16-3264-8_19
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