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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 382619, 12 pages
Research Article

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

1School of Information Engineering, Nanchang University, Nanchang 330031, China
2Department of Electrical and Computer Engineering, University of Calgary, Calgary, Canada T2N 1N4

Received 29 March 2013; Revised 29 May 2013; Accepted 17 June 2013

Academic Editor: Hua Li

Copyright © 2013 Minxiang Liu et al. 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.


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.