Convolutional neural network-based place recognition
Chen, Zetao, Lam, Obadiah, Jacobson, Adam, & Milford, Michael (2014) Convolutional neural network-based place recognition. In Chen, C (Ed.) Proceedings of the 16th Australasian Conference on Robotics and Automation 2014. Australian Robotics and Automation Association (ARAA), Australia, pp. 1-8.
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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 | ||||
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| Deposited On: | 05 Jan 2015 01:00 | ||||
| Last Modified: | 18 Mar 2025 20:19 |
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