Convolutional neural network-based place recognition
arXiv preprint arXiv:1411.1509, 2014•arxiv.org
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 …
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 …
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.
arxiv.org