Next Article in Journal
Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study
Next Article in Special Issue
Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm
Previous Article in Journal
Automatic Shadow Detection in Urban Very-High-Resolution Images Using Existing 3D Models for Free Training
Previous Article in Special Issue
Use of a Wearable Mobile Laser System in Seamless Indoor 3D Mapping of a Complex Historical Site
Article Menu
Issue 1 (January-1) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(1), 73;

Indoor Topological Localization Using a Visual Landmark Sequence

Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geoinformation, Shenzhen University, Shenzhen 518060, China
School of Computer Science, The University of Nottingham, Nottingham NG8 1BB, UK
College of Information Engineering & Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
International Doctoral Innovation Centre & School of Computer Science, The University of Nottingham Ningbo China, Ningbo 315100, China
College of Resource and Environment, Henan University of Economics and Law, Zhengzhou 450046, China
Author to whom correspondence should be addressed.
Received: 7 November 2018 / Revised: 30 December 2018 / Accepted: 31 December 2018 / Published: 3 January 2019
(This article belongs to the Special Issue Mobile Mapping Technologies)
Full-Text   |   PDF [8704 KB, uploaded 3 January 2019]   |  


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. View Full-Text
Keywords: visual landmark sequence; indoor topological localization; convolutional neural network (CNN); second order hidden Markov model visual landmark sequence; indoor topological localization; convolutional neural network (CNN); second order hidden Markov model

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zhu, J.; Li, Q.; Cao, R.; Sun, K.; Liu, T.; Garibaldi, J.M.; Li, Q.; Liu, B.; Qiu, G. Indoor Topological Localization Using a Visual Landmark Sequence. Remote Sens. 2019, 11, 73.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top