A Novel Feature-Level Data Fusion Method for Indoor Autonomous Localization

Mathematical Problems in Engineering. 2013;2013 DOI 10.1155/2013/382619

 

Journal Homepage

Journal Title: Mathematical Problems in Engineering

ISSN: 1024-123X (Print); 1563-5147 (Online)

Publisher: Hindawi Publishing Corporation

LCC Subject Category: Technology: Engineering (General). Civil engineering (General) | Science: Mathematics

Country of publisher: Egypt

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Minxiang Liu (School of Information Engineering, Nanchang University, Nanchang 330031, China)
Yuhao Wang (School of Information Engineering, Nanchang University, Nanchang 330031, China)
Henry Leung (Department of Electrical and Computer Engineering, University of Calgary, Calgary, T2N 1N4, Canada)
Jiangnan Yu (School of Information Engineering, Nanchang University, Nanchang 330031, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 26 weeks

 

Abstract | Full Text

We present a novel feature-level data fusion method for autonomous localization in an inactive multiple reference unknown indoor environment. Since monocular sensors cannot provide the depth information directly, the proposed method incorporates the edge information of images from a camera with homologous depth information received from an infrared sensor. Real-time experimental results demonstrate that the accuracies of position and orientation are greatly improved by using the proposed fusion method in an unknown complex indoor environment. Compared to monocular localization, the proposed method is found to have up to 70 percent improvement in accuracy.