CASIA OpenIR
Real-Time Visual Place Recognition Based on Analyzing Distribution of Multi-scale CNN Landmarks
Xin, Zhe1,2; Cui, Xiaoguang1; Zhang, Jixiang1; Yang, Yiping1; Wang, Yanqing1
Source PublicationJOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
ISSN0921-0296
2019-06-01
Volume94Issue:3-4Pages:777-792
Corresponding AuthorXin, Zhe(xinzhe2015@ia.ac.cn)
AbstractWhat makes visual place recognition difficult to solve is the variation of the real-world places. In this work, an effective similarity measurement is proposed for visual place recognition in changing environments, based on Convolutional Neural Networks (CNNs) and content-based multi-scale landmarks. The image is firstly segmented into multi-scale landmarks with content information in order to adapt variations of viewpoint, then highly representative features of landmarks are derived from Convolutional Neural Networks (CNNs), which are robust against appearance variations. In the similarity measurement, the similarity between images is determined by analyzing both spatial and scale distributions of matched landmarks. Moreover, an efficient feature extraction and reduction strategy are proposed to generate all features of landmarks at one time. The efficiency of the proposed method makes it suitable for real-time applications. The proposed method is evaluated on two widespread datasets with varied viewpoint and appearance conditions and achieves superior performance against four other state-of-the-art methods, such as the bag-of-words model DBoW3 and the CNN-based Edge Boxes landmarks. Extensive experimentation demonstrates that integrating global and local information can provide more invariance in severe appearance changes, and considering the spatial distribution of landmarks can improve the robustness against viewpoint changes.
KeywordVisual place recognition Localization Convolutional neural networks Changing environments Landmark distribution 68T45 92B20
DOI10.1007/s10846-018-0804-x
WOS KeywordFEATURES ; SCALE
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Robotics
WOS SubjectComputer Science, Artificial Intelligence ; Robotics
WOS IDWOS:000468432500015
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24213
Collection中国科学院自动化研究所
Corresponding AuthorXin, Zhe
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
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,Cui, Xiaoguang,Zhang, Jixiang,et al. Real-Time Visual Place Recognition Based on Analyzing Distribution of Multi-scale CNN Landmarks[J]. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS,2019,94(3-4):777-792.
APA Xin, Zhe,Cui, Xiaoguang,Zhang, Jixiang,Yang, Yiping,&Wang, Yanqing.(2019).Real-Time Visual Place Recognition Based on Analyzing Distribution of Multi-scale CNN Landmarks.JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS,94(3-4),777-792.
MLA Xin, Zhe,et al."Real-Time Visual Place Recognition Based on Analyzing Distribution of Multi-scale CNN Landmarks".JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 94.3-4(2019):777-792.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xin, Zhe]'s Articles
[Cui, Xiaoguang]'s Articles
[Zhang, Jixiang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xin, Zhe]'s Articles
[Cui, Xiaoguang]'s Articles
[Zhang, Jixiang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xin, Zhe]'s Articles
[Cui, Xiaoguang]'s Articles
[Zhang, Jixiang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.