Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-26T07:13:45.679Z Has data issue: false hasContentIssue false
  • English
  • Français

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)
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Agarwal, V., Abidi, B.R., Koschan, A. and Abidi, M.A. (2006). An overview of color constancy algorithms. Journal of Pattern Recognition Research, 1(1), 42–54.CrossRefGoogle Scholar
Barnard, K., Cardei, V. and Funt, B. (2002). A comparison of computational color constancy algorithms -part I: Methodology and experiments with synthesized data. Image Processing, IEEE Transactions on, 11(9), 972–984.CrossRefGoogle ScholarPubMed
Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.CrossRefGoogle Scholar
Bonin-Font, F., Ortiz, A. and Oliver, G. (2008). Visual navigation for mobile robots: A survey. Journal of Intelligent and Robotic Systems, 53(3), 263–296.CrossRefGoogle Scholar
Buchsbaum, G. (1980). A spatial processor model for object colour perception. Journal of the Franklin Institute, 310(1), 1–26.CrossRefGoogle Scholar
Calloway, T.M., Eichel, P.H. and Jakowatz, C.V. Jr. (1990). Iterative registration of SAR imagery. In: San Diego'90 , 8–13 July, International Society for Optics and Photonics, 412–420.Google Scholar
Conte, G. and Doherty, P. (2011). A visual navigation system for UAS based on geo-referenced imagery. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 3822, 101–106.Google Scholar
Ebner, M. (2004). A parallel algorithm for color constancy. Journal of Parallel and Distributed Computing, 64(1), 79–88.CrossRefGoogle Scholar
Jin, Z., Pan, Q., Zhao, C. and Liu, Y. (2013). Suitability analysis based on multi-feature fusion visual saliency model in vision navigation. In: Information Fusion (FUSION), 2013 16th International Conference on, 235–241.Google Scholar
Jin, Z., Wang, X., Moran, W., Pan, Q. and Zhao, C. (2014a). Efficient scene matching using salient regions under spatial constraints. In: Information Fusion (FUSION), 2014 17th International Conference on, 1–8.Google Scholar
Jin, Z., Wang, X., Morelande, M., Moran, W., Pan, Q. and Zhao, C. (2014b). Landmark selection for scene matching with knowledge of color histogram. In: Information Fusion (FUSION), 2014 17th International Conference on, 1–8.Google Scholar
Jin, Z., Pan, Q., Zhao, C., Wei, Y. and Ma, J. (2015). Color constancy algorithm based on local exact histogram matching for scene matching navigation of UAVs. Journal of Chinese Inertial Technology, 23(5), 674–680.Google Scholar
Jwo, D.J., Chung, F.C. and Yu, K.L. (2013). GPS/INS Integration Accuracy Enhancement Using the Interacting Multiple Model Nonlinear Filters. Journal of Applied Research and Technology, 11(4), 496–509.CrossRefGoogle Scholar
Li, Y., Yu, Y., Pan, Q. and Zhao, C. (2009). Scene matching based on spatial relation constraint in suitable-matching area. In: Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on, 4, 598–603.Google Scholar
Lincheng, S., Yanlong, B., Xin, X. and Liang, P. (2010). Research on matching-area suitability for scene matching aided navigation. Acta Aeronautica et Astronautica Sinica, 31(3), 553–563.Google Scholar
Ling, Z., Pan, Q., Zhang, S., Liang, Y. and Li, Y. (2009). A scene matching method with weighted hausdorff distance based on edge measure. Journal of Astronautics, 4, 053.Google Scholar
Lo, T.K. and Gerson, G. (1979). Guidance system position update by multiple subarea correlation. In: 1979 Huntsville Technical Symposium, International Society for Optics and Photonics, 30–40.Google Scholar
Morovic, J., Shaw, J. and Sun, P.L. (2002). A fast, non-iterative and exact histogram matching algorithm. Pattern Recognition Letters, 23(1), 127–135.CrossRefGoogle Scholar
Nemra, A. and Aouf, N. (2009). Robust airborne 3d visual simultaneous localisation and mapping with observability and consistency analysis. Journal of Intelligent and Robotic Systems, 55(4–5), 345–376.CrossRefGoogle Scholar
Siagian, C. and Itti, L. (2009). Biologically inspired mobile robot vision localisation. Robotics , IEEE Transactions on, 25(4), 861–873.Google Scholar
Sim, D.G., Park, R.H., Kim, R.C., Lee, S.U. and Kim, I.C. (2002). Integrated position estimation using aerial image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 1–18.Google Scholar
Van De Weijer, J., Gevers, T. and Gijsenij, A. (2007). Edge-based color constancy. Image Processing, IEEE Transactions on, 16(9), 2207–2214.CrossRefGoogle ScholarPubMed
Wang, T., Wang, C., Liang, J., Chen, Y. and Zhang, Y. (2013). Vision-aided inertial navigation for small unmanned aerial vehicles in GPS-denied environments. International Journal of Advanced Robotic Systems, 10, 1–12.CrossRefGoogle Scholar
Williams, B. and Reid, I. (2010). On combining visual slam and visual odometry. In: Robotics and Automation (ICRA), 2010 IEEE International Conference on, 3494–3500.CrossRefGoogle Scholar
Zhao, F., Huang, Q. and Gao, W. (2006). Image matching by normalized cross-correlation. In: Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, 2, II729–II732.Google Scholar