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Multi-Region Scene Matching Based Localisation for Autonomous Vision Navigation of UAVs

Published online by Cambridge University Press:  18 April 2016

Zhenlu Jin ,
Xuezhi Wang ,
Bill Moran ,
Quan Pan  and
Chunhui Zhao
Zhenlu Jin
Affiliation:
(School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China) (Department of Electrical and Electronic Engineering, University of Melbourne, Australia)
Xuezhi Wang
Affiliation:
(School of Engineering, RMIT University, Australia)
Bill Moran
Affiliation:
(School of Engineering, RMIT University, Australia)
Quan Pan
Affiliation:
(School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China)
Chunhui Zhao*
Affiliation:
(School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China)
*
(E-mail: zhaochunhuinpu@126.com)
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Abstract

A multi-region scene matching-based localisation system for automated navigation of Unmanned Aerial Vehicles (UAV) is proposed. This system may serve as a backup navigation error correction system to support autonomous navigation in the absence of a global positioning system such as a Global Navigation Satellite System. Conceptually, the system computes the location of the UAV by comparing the sensed images taken by an on board optical camera with a library of pre-recorded geo-referenced images. Several challenging issues in building such a system are addressed, including the colour variability problem and elimination of time-varying details from the pairs of images. The overall algorithm is an iterative process involving four sub-processes: firstly, exact histogram matching is applied to sensed images to overcome the colour variability issues; secondly, regions are automatically extracted from the sensed image where landmarks are detected via their colour histograms; thirdly, these regions are matched against the library, while eliminating inconsistent regions between underlying image pairs in the registration process; and finally the location of the UAV is computed using an optimisation procedure which minimises the localisation error using affine transformations. Experimental results demonstrate the proposed system in terms of accuracy, robustness and computational efficiency.

Keywords

Scene MatchingColour ConstancyLandmark DetectionMulti-Region
Type
Research Article
Information
The Journal of Navigation , Volume 69 , Issue 6 , November 2016 , pp. 1215 - 1233
Copyright
Copyright © The Royal Institute of Navigation 2016 

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