中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Visual Place Recognition with CNNs: From Global to Partial

文献类型:会议论文

作者Zhe, Xin1,2; Xiaoguang, Cui2; Jixiang, Zhang2; Yiping, Yang2; Yanqing, Wang2; Zhang, Jixiang; Cui, Xiaoguang; Wang, Yanqing; Yang, Yiping; Xin, Zhe
出版日期2017
会议日期November 28th to December 1st
会议地点Montreal, Canada
英文摘要

Visual place recognition is one of the most challenging problems in computer vision, due to the large diversities that real-world places can represent. Recently, visual place recognition has become a key part of loop closure detection and topological localization in long-term mobile robot autonomy. In this work, we build up a novel visual place recognition pipeline composed of a first filtering stage followed by a partial re-ranking process. In the filtering stage, image-wise features are utilized to find a small set of potential places. Afterwards, stable region-wise landmarks are extracted for more accurate matching in the partial re-ranking process. All global and partial image representations are derived from pre-trained Convolutional Neural Networks (CNNs), and the landmarks are extracted by object proposal techniques. Moreover, a new similarity measurement is provided by considering both spatial and scale distribution of landmarks. Compared with current methods only considering scale distribution, the presented similarity measurement can benefit recognition precision and robustness effectively. Experiments with varied viewpoints and environmental conditions demonstrate that the proposed method achieves superior performance against state-of-the-art methods.

源URL[http://ir.ia.ac.cn/handle/173211/39243]  
专题综合信息系统研究中心_视知觉融合及其应用
通讯作者Xiaoguang, Cui; Cui, Xiaoguang
作者单位1.University of Chinese Academy of Sciences, Beijing, China
2.Institute of Automation, Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Zhe, Xin,Xiaoguang, Cui,Jixiang, Zhang,et al. Visual Place Recognition with CNNs: From Global to Partial[C]. 见:. Montreal, Canada. November 28th to December 1st.

入库方式: OAI收割

来源:自动化研究所

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