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New Environmental Line Feature-based Vision Navigation: Design and Analysis

Published online by Cambridge University Press:  09 May 2017

Zeyu Li ,
Jinling Wang ,
Kai Chen  and
Yu Sun
Zeyu Li*
Affiliation:
(School of Civil and Environmental Engineering, UNSW Australia, Sydney, Australia)
Jinling Wang
Affiliation:
(School of Civil and Environmental Engineering, UNSW Australia, Sydney, Australia)
Kai Chen
Affiliation:
(School of Civil and Environmental Engineering, UNSW Australia, Sydney, Australia)
Yu Sun
Affiliation:
(School of Civil and Environmental Engineering, UNSW Australia, Sydney, Australia)
*
(E-mail: zeyu.li@student.unsw.edu.au)
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Abstract

Vision navigation using environmental features has been widely applied when satellite signals are not available. However, the matching performance of traditional environmental features such as keypoints degrades significantly in weakly textured areas, deteriorating navigation performance. Further, the user needs to evaluate and assure feature matching quality. In this paper, a new feature, named Line Segment Intersection Feature (LSIF), is proposed to solve the availability problem in weakly textured regions. Then a combined descriptor involving global structure and local gradient is designed for similarity comparison. To achieve reliable point-to-point matching, a coarse-to-fine matching algorithm is developed, which improves the performance of the point set matching algorithm. Finally, a framework of matching quality evaluation is proposed to assure matching performance. Through the comparison, it is demonstrated that the proposed new feature has superior overall performance especially on correctly matched numbers of keypoints and matching correctness. Also, using real image sets with weak texture, it is shown that the proposed LSIF can achieve improved navigation solutions with high continuity and accuracy.

Keywords

VisionIndoor navigationWeak textureLine segment intersection feature
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 5 , September 2017 , pp. 1133 - 1152
Copyright
Copyright © The Royal Institute of Navigation 2017 

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