Localizing Discriminative Visual Landmarks for Place Recognition

We address the problem of visual place recognition with perceptual changes. The fundamental problem of visual place recognition is generating robust image representations which are not only insensitive to environmental changes but also distinguishable to different places. Taking advantage of the fea...

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Bibliographic Details
Published in2019 International Conference on Robotics and Automation (ICRA) pp. 5979 - 5985
Main Authors Xin, Zhe, Cai, Yinghao, Lu, Tao, Xing, Xiaoxia, Cai, Shaojun, Zhang, Jixiang, Yang, Yiping, Wang, Yanqing
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Buildings
Feature extraction
Image recognition
Measurement
Task analysis
Training
Visualization
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Summary:We address the problem of visual place recognition with perceptual changes. The fundamental problem of visual place recognition is generating robust image representations which are not only insensitive to environmental changes but also distinguishable to different places. Taking advantage of the feature extraction ability of Convolutional Neural Networks (CNNs), we further investigate how to localize discriminative visual landmarks that positively contribute to the similarity measurement, such as buildings and vegetations. In particular, a Landmark Localization Network (LLN) is designed to indicate which regions of an image are used for discrimination. Detailed experiments are conducted on open source datasets with varied appearance and viewpoint changes. The proposed approach achieves superior performance against state-of-the-art methods.
ISSN:2577-087X
DOI:10.1109/ICRA.2019.8794383