LoST? Appearance-invariant place recognition for opposite viewpoints using visual semantics
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Description
Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance. This capability is especially apparent during driving: a human driver can recognize where they are when travelling in the reverse direction along a route for the first time, without having to turn back and look. The difficulty of this problem exceeds any addressed in past appearance- and viewpoint-invariant visual place recognition (VPR) research, in part because large parts of the scene are not commonly observable from opposite directions. Consequently, as shown in this paper, the precision-recall performance of current state-of-the-art viewpoint- and appearance-invariant VPR techniques is orders of magnitude below what would be usable in a closed-loop system. Current engineered solutions predominantly rely on panoramic camera or LIDAR sensing setups; an eminently suitable engineering solution but one that is clearly very different to how humans navigate, which also has implications for how naturally humans could interact and communicate with the navigation system. In this paper we develop a suite of novel semantic- and appearance-based techniques to enable for the first time high performance place recognition in this challenging scenario. We first propose a novel Local Semantic Tensor (LoST) descriptor of images using the convolutional feature maps from a state-of-the-art dense semantic segmentation network. Then, to verify the spatial semantic arrangement of the top matching candidates, we develop a novel approach for mining semantically-salient keypoint correspondences.
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ID Code: | 124630 | ||||||
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Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||||
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Measurements or Duration: | 10 pages | ||||||
DOI: | 10.15607/RSS.2018.XIV.022 | ||||||
ISBN: | 978-0-9923747-4-7 | ||||||
Pure ID: | 33310350 | ||||||
Divisions: | Past > Institutes > Institute for Future Environments Past > QUT Faculties & Divisions > Science & Engineering Faculty |
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Funding: | |||||||
Copyright Owner: | Consult author(s) regarding copyright matters | ||||||
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: | 15 Jan 2019 04:10 | ||||||
Last Modified: | 28 Jul 2024 22:50 |
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