Semantic-geometric visual place recognition: a new perspective for reconciling opposing views
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33498036. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
Human drivers are capable of recognizing places from a previous journey even when viewing them from the opposite direction during the return trip under radically different environmental conditions, without needing to look back or employ a (Formula presented.) camera or LIDAR sensor. Such navigation capabilities are attributed in large part to the robust semantic scene understanding capabilities of humans. However, for an autonomous robot or vehicle, achieving such human-like visual place recognition capability presents three major challenges: (1) dealing with a limited amount of commonly observable visual content when viewing the same place from the opposite direction; (2) dealing with significant lateral viewpoint changes caused by opposing directions of travel taking place on opposite sides of the road; and (3) dealing with a radically changed scene appearance due to environmental conditions such as time of day, season, and weather. Current state-of-the-art place recognition systems have only addressed these three challenges in isolation or in pairs, typically relying on appearance-based, deep-learnt place representations. In this paper, we present a novel, semantics-based system that for the first time solves all three challenges simultaneously. We propose a hybrid image descriptor that semantically aggregates salient visual information, complemented by appearance-based description, and augment a conventional coarse-to-fine recognition pipeline with keypoint correspondences extracted from within the convolutional feature maps of a pre-trained network. Finally, we introduce descriptor normalization and local score enhancement strategies for improving the robustness of the system. Using both existing benchmark datasets and extensive new datasets that for the first time combine the three challenges of opposing viewpoints, lateral viewpoint shifts, and extreme appearance change, we show that our system can achieve practical place recognition performance where existing state-of-the-art methods fail.
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ID Code: | 133595 | ||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||
Refereed: | Yes | ||||||
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Measurements or Duration: | 26 pages | ||||||
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DOI: | 10.1177/0278364919839761 | ||||||
ISSN: | 1741-3176 | ||||||
Pure ID: | 33498036 | ||||||
Divisions: | Current > Research Centres > Centre for Robotics Current > Research Centres > Centre for Future Mobility/CARRSQ Past > Institutes > Institute for Future Environments Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Electrical Engineering & Robotics Current > QUT Faculties and Divisions > Faculty of Health |
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Copyright Owner: | 2019 The Author(s) | ||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||
Deposited On: | 14 Oct 2019 00:38 | ||||||
Last Modified: | 24 Jun 2024 19:54 |
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