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Indoor Topological Localization Using a Visual Landmark Sequence

Zhu, Jiasong; Li, Qing; Cao, Rui; Sun, Ke; Liu, Tao; Garibaldi, Jonathan; Li, Qingquan; Liu, Bozhi; Qiu, Guoping


Jiasong Zhu

Qing Li

Rui Cao

Ke Sun

Tao Liu

Qingquan Li

Bozhi Liu

Professor of Visual Information Processing


© 2019 by the authors. 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.


Zhu, J., Li, Q., Cao, R., Sun, K., Liu, T., Garibaldi, J., …Qiu, G. (2019). Indoor Topological Localization Using a Visual Landmark Sequence. Remote Sensing, 11(1), Article 73.

Journal Article Type Article
Acceptance Date Dec 30, 2018
Online Publication Date Jan 3, 2019
Publication Date Jan 3, 2019
Deposit Date Jan 11, 2019
Publicly Available Date Jan 11, 2019
Journal Remote Sensing
Electronic ISSN 2072-4292
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 1
Article Number 73
Keywords General Earth and Planetary Sciences
Public URL
Publisher URL


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