文献类型：期刊文献

英文题名：Indoor Topological Localization Using a Visual Landmark Sequence

作者：Zhu, Jiasong[1,2];Li, Qing[1,2,3,4,5];Cao, Rui[1,2,4,5,6,7];Sun, Ke[1,2];Liu, Tao[8];Garibaldi, Jonathan M.[3];Li, Qingquan[1,2];Liu, Bozhi[4,5];Qiu, Guoping[3,4,5]

第一作者：Zhu, Jiasong

通讯作者：Li, Q[1];Li, Q[2];Li, Q[3];Li, Q[4];Li, Q[5]

机构：[1]Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China;[2]Shenzhen Univ, Key Lab Geoenvironm Monitoring Coastal Zone, Natl Adm Surveying Mapping & Geoinformat, Shenzhen 518060, Peoples R China;[3]Univ Nottingham, Sch Comp Sci, Nottingham NG8 1BB, England;[4]Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China;[5]Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China;[6]Univ Nottingham Ningbo China, Int Doctoral Innovat Ctr, Ningbo 315100, Zhejiang, Peoples R China;[7]Univ Nottingham Ningbo China, Sch Comp Sci, Ningbo 315100, Zhejiang, Peoples R China;[8]Henan Univ Econ & Law, Coll Resource & Environm, Zhengzhou 450046, Henan, Peoples R China

第一机构：Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China

通讯机构：[1]corresponding author), Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China;[2]corresponding author), Shenzhen Univ, Key Lab Geoenvironm Monitoring Coastal Zone, Natl Adm Surveying Mapping & Geoinformat, Shenzhen 518060, Peoples R China;[3]corresponding author), Univ Nottingham, Sch Comp Sci, Nottingham NG8 1BB, England;[4]corresponding author), Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China;[5]corresponding author), Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen 518060, Peoples R China.

年份：2019

卷号：11

期号：1

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20190306385476);Scopus(收录号：2-s2.0-85059947513);WOS:【SCI-EXPANDED(收录号:WOS:000457935600073)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 41871329, in part by the Shenzhen Future Industry Development Funding Program under Grant 201607281039561400, in part by the Shenzhen Scientific Research and Development Funding Program under Grant JCYJ20170818092931604, and in part by the Horizon Centre for Doctoral Training at the University of Nottingham (RCUK Grant No. EP/L015463/1).

语种：英文

外文关键词：visual landmark sequence; indoor topological localization; convolutional neural network (CNN); second order hidden Markov model

摘要：This paper presents a novel indoor topological localization method based on mobile phone videos. Conventional methods suffer from indoor dynamic environmental changes and scene ambiguity. The proposed Visual Landmark Sequence-based Indoor Localization (VLSIL) method is capable of addressing problems by taking steady indoor objects as landmarks. Unlike many feature or appearance matching-based localization methods, our method utilizes highly abstracted landmark sematic information to represent locations and thus is invariant to illumination changes, temporal variations, and occlusions. We match consistently detected landmarks against the topological map based on the occurrence order in the videos. The proposed approach contains two components: a convolutional neural network (CNN)-based landmark detector and a topological matching algorithm. The proposed detector is capable of reliably and accurately detecting landmarks. The other part is the matching algorithm built on the second order hidden Markov model and it can successfully handle the environmental ambiguity by fusing sematic and connectivity information of landmarks. To evaluate the method, we conduct extensive experiments on the real world dataset collected in two indoor environments, and the results show that our deep neural network-based indoor landmark detector accurately detects all landmarks and is expected to be utilized in similar environments without retraining and that VLSIL can effectively localize indoor landmarks.