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Authors: Farid Alijani 1 ; Jukka Peltomäki 1 ; Jussi Puura 2 ; Heikki Huttunen 3 ; Joni-Kristian Kämäräinen 1 and Esa Rahtu 1

Affiliations: 1 Tampere University, Finland ; 2 Sandvik Mining and Construction Ltd, Finland ; 3 Visy Oy, Finland

Keyword(s): Tracking and Visual Navigation, Content-Based Indexing, Search, and Retrieval, Deep Convolutional Neural Network, Deep Learning for Visual Understanding.

Abstract: In this paper, we provide a comprehensive study on evaluating two state-of-the-art deep metric learning methods for visual place recognition. Visual place recognition is an essential component in the visual localization and the vision-based navigation where it provides an initial coarse location. It is used in variety of autonomous navigation technologies, including autonomous vehicles, drones and computer vision systems. We study recent visual place recognition and image retrieval methods and utilize them to conduct extensive and comprehensive experiments on two diverse and large long-term indoor and outdoor robot navigation datasets, e.g., COLD and Oxford Radar RobotCar along with ablation studies on the crucial parameters of the deep architectures. Our comprehensive results indicate that the methods can achieve 5 m of outdoor and 50 cm of indoor place recognition accuracy with high recall rate of 80 %.

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Paper citation in several formats:
Alijani, F.; Peltomäki, J.; Puura, J.; Huttunen, H.; Kämäräinen, J. and Rahtu, E. (2022). Evaluation of Long-term Deep Visual Place Recognition. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 437-447. DOI: 10.5220/0010834700003124

@conference{visapp22,
author={Farid Alijani. and Jukka Peltomäki. and Jussi Puura. and Heikki Huttunen. and Joni{-}Kristian Kämäräinen. and Esa Rahtu.},
title={Evaluation of Long-term Deep Visual Place Recognition},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={437-447},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010834700003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Evaluation of Long-term Deep Visual Place Recognition
SN - 978-989-758-555-5
IS - 2184-4321
AU - Alijani, F.
AU - Peltomäki, J.
AU - Puura, J.
AU - Huttunen, H.
AU - Kämäräinen, J.
AU - Rahtu, E.
PY - 2022
SP - 437
EP - 447
DO - 10.5220/0010834700003124
PB - SciTePress

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