Indoor topological localization using a visual landmark sequence

Zhu, Jiasong and Li, Qing and Cao, Rui and Sun, Ke and Liu, Tao and Garibaldi, Jonathan and Li, Qingquan and Liu, Bozhi and Qiu, Guoping (2019) Indoor topological localization using a visual landmark sequence. Remote Sensing, 11 (1). p. 73. ISSN 2072-4292

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution.
Download (8MB) | Preview

Abstract

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.

Item Type: Article
Keywords: visual landmark sequence; indoor topological localization; onvolutional neural network(CNN); second order hidden Markov model
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
University of Nottingham Ningbo China > Graduate School
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.3390/rs11010073
Related URLs:
URLURL Type
UNSPECIFIEDPublisher
Depositing User: Wu, Cocoa
Date Deposited: 04 Mar 2019 09:45
Last Modified: 04 Mar 2019 09:45
URI: http://eprints.nottingham.ac.uk/id/eprint/56196

Actions (Archive Staff Only)

Edit View Edit View