CASIA OpenIR  > 综合信息系统研究中心  > 视知觉融合及其应用
Localizing Discriminative Visual Landmarks for Place Recognition
Xin, Zhe1,2; Cai, Yinghao1; Lu, Tao1; Xing, Xiaoxia1,2; Cai, Shaojun3; Zhang, Jixiang1; Yang, Yiping1; Wang, Yanqing1
2019
Conference Name2019 International Conference on Robotics and Automation (ICRA)
Conference DateMay 20-24, 2019
Conference PlacePalais des congres de Montreal, Montreal, Canada
Abstract

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.

Indexed ByEI
Language英语
Sub direction classification目标检测、跟踪与识别
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39239
Collection综合信息系统研究中心_视知觉融合及其应用
Corresponding AuthorZhang, Jixiang
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
3.UISEE Technologies Beijing Co., Ltd
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xin, Zhe,Cai, Yinghao,Lu, Tao,et al. Localizing Discriminative Visual Landmarks for Place Recognition[C],2019.
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