Convolutional Neural Network-based place recognition
Chen, Zetao, Lam, Obadiah, Jacobson, Adam, & Milford, Michael (2014) Convolutional Neural Network-based place recognition. In Australasian Conference on Robotics and Automation 2014, 2-4 December 2014, The University of Melbourne, Victoria, Australia.
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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|Item Type:||Conference Paper|
|Keywords:||Convolutional Neural Network, Place Recognition|
|Divisions:||Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 [please consult the authors]|
|Deposited On:||05 Jan 2015 01:00|
|Last Modified:||13 Jan 2015 20:01|
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