Only look once, mining distinctive landmarks from ConvNet for visual place recognition
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Author / Producer
Date
2017
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Abstract
Recently, image representations derived from Convolutional Neural Networks (CNNs) have been demonstrated to achieve impressive performance on a wide variety of tasks, including place recognition. In this paper, we take a step deeper into the internal structure of CNNs and propose novel CNN-based image features for place recognition by identifying salient regions and creating their regional representations directly from the convolutional layer activations. A range of experiments is conducted on challenging datasets with varied conditions and viewpoints. These reveal superior precision-recall characteristics and robustness against both viewpoint and appearance variations for the proposed approach over the state of the art. By analyzing the feature encoding process of our approach, we provide insights into what makes an image presentation robust against external variations.
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Publication status
published
External links
Editor
Book title
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
9 - 16
Publisher
IEEE
Event
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Place Recognition; Convolutional Neural Network; Feature Encoding; Robot Localization
Organisational unit
09559 - Chli, Margarita (ehemalig) / Chli, Margarita (former)
Notes
Funding
644128 - Collaborative Aerial Robotic Workers (SBFI)