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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: [email protected])
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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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References

REFERENCES

Belongie, S., Malik, J. and Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 509–522.Google Scholar
Hartley, R. and Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge University Press.Google Scholar
Humenberger, M., Engelke, T. and Kubinger, W. (2010). A census-based stereo vision algorithm using modified Semi-Global Matching and plane fitting to improve matching quality. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 77–84.Google Scholar
Jin, Z., Wang, X., Moran, B., Pan, Q. and Zhao, C. (2016). Multi-region scene matching based localisation for autonomous vision navigation of UAVs. Journal of Navigation, 69, 1215–1233.Google Scholar
Kim, H. and Lee, S. (2012). Simultaneous line matching and epipolar geometry estimation based on the intersection context of coplanar line pairs. Pattern Recognition Letters, 33, 1349–1363.Google Scholar
Knight, N.L., Wang, J. and Rizos, C. (2010). Generalised measures of reliability for multiple outliers. Journal of Geodesy, 84, 625–635.CrossRefGoogle Scholar
Kuhn, H.W. (1955). The Hungarian method for the assignment problem. Naval research logistics quarterly, 2, 83–97.Google Scholar
Lewis, J. (1995). Fast normalized cross-correlation. Vision interface, 120–123.Google Scholar
Li, X., Wang, J., Knight, N. and Ding, W. (2011). Vision-based positioning with a single camera and 3D maps: accuracy and reliability analysis. Journal of Global Positioning Systems, 10, 19–29.Google Scholar
Li, Z., Wang, J., Alqurashi, M., Chen, K. and Zheng, S. (2016). Geometric analysis of reality-based indoor 3D mapping. Journal of Global Positioning Systems, 14, 1.Google Scholar
Liu, C., Zhou, F., Sun, Y., Di, K. and Liu, Z. (2012). Stereo-image matching using a speeded up robust feature algorithm in an integrated vision navigation system. Journal of Navigation, 65, 671–692.Google Scholar
Lowe, D.G. (1999). Object recognition from local scale-invariant features. The proceedings of the 7th IEEE international conference on Computer vision, 1999. 1150–1157.Google Scholar
Myronenko, A. and Song, X. (2010). Point set registration: Coherent point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 2262–2275.Google Scholar
Thompson, E. (1966). Space resection: Failure cases. The Photogrammetric Record, 5, 201–207.Google Scholar
Von Gioi, R.G., Jakubowicz, J., Morel, J.-M. and Randall, G. (2008). LSD: A fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 722–732.Google Scholar
Xian, Z., Hu, X. and Lian, J. (2015). Fusing stereo camera and low-cost inertial measurement unit for autonomous navigation in a tightly-coupled approach. Journal of Navigation, 68, 434–452.Google Scholar
Zhou, H., Zou, D., Pei, L., Ying, R., Liu, P. and Yu, W. (2015). StructSLAM: Visual SLAM With Building Structure Lines. IEEE Transactions on Vehicular Technology, 64, 1364–1375.Google Scholar