Convolutional neural network-based place recognition
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CNN-Based_Place_Recognition.pdf. |
Description
Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.
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| ID Code: | 79662 | ||||
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| Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||
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| Measurements or Duration: | 8 pages | ||||
| Keywords: | Convolutional Neural Network, Place Recognition | ||||
| Pure ID: | 32644422 | ||||
| Divisions: | Past > Institutes > Institute for Future Environments Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Research Centres > ARC Centre of Excellence for Robotic Vision Current > Research Centres > Centre for Tropical Crops and Biocommodities |
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| 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: | 05 Jan 2015 01:00 | ||||
| Last Modified: | 16 Jul 2024 16:59 |
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