A real-time simple light detection application for a flying robot in extreme noise and interference

Abstract

In this work, a real-time vision-based algorithm has been developed and implemented on a flying robot, in order to detect and identify a light beacon in the presence of excessive colored noise and interference. Starting from very basic and simple image analysis techniques including color histograms, filtering techniques, and color space analyses, typical pixel-based characteristics or a model of the light beacon has been progressively established. It has been found that not only are various color space-based characteristics significant, but also the relationships between various channels across different color spaces are of great consequence, in a beacon detection problem, specifically referring to a blue light-emitting diode. A block-based search algorithm comprising of multiple thresholds and linear confidence level calculation has been implemented to search established model characteristics in real-time video image data. During implementation, once excessive noise was encountered during flight tests, a simple and low cost noise and interference filter was developed. This filter very effectively handled all noise encountered in real time. The proposed work was successfully implemented and utilized on GeorgiaTech’s participating aircraft for the International Aerial Robotics Competition by Association for Unmanned Vehicle Systems International for detection of a blue light-emitting diode problem. Major contributions of this work include establishing a multiple threshold search and detection algorithm based on not only various color channels but also their relationships and handling of as much as 40% noisy or interfered video data with successful practical implementation and demonstration of proposed approach.

Keywords

Light detection locating visual marker search beacon flying robot rule-based identification blue LED

Get full access to this article

View all access options for this article.

References

Tippetts

Lee

Archibald

. An on-board vision sensor system for small unmanned vehicle applications. Proc Mach Vis Appl 2012; 23: 403–415.

http://iarc.angel-strike.com/oldauvs/5th_mission/index.php# (accessed October 2009).

Sivic

Bryan

Alexei

. Discovering objects and their location in images. Proc Int Conf Comput Vis 2005; 1: 370–377.

Csurka G, Dance C, Fan L, et al. Visual categorization with bags of key points. In: Proceeding of ECCV international workshop on statistical learning in computer vision, Prague, Czech Republic, 2004, pp.1–22. Prague, Czech Republic: ECCV.

Candamo

Kasturi

Goldgof

. Using color profiles for street detection in low-altitude UAV video. Proc SPIE Int Soc Opt Eng 2009; 7307: 10–12.

Chung

Lin

Huang

. Efficient shadow detection of color aerial images based on successive thresholding scheme. IEEE Trans Geosci Remote Sens 2009; 47(2): 671–682.

Juang

Sun

Chen

. Object detection by color histogram-based fuzzy classifier with support vector learning. Neurocomputing 2009; 72(10–12): 2464–2476.

Chai

Yin

. Method of road recognition based on color image edge detection. Comput Eng Appl 2008; 44(28): 177–179, 183.

Celik T and Kai-Kuang M. Computer vision based fire detection in color images. In: IEEE conference on soft computing in industrial applications, SMCia/08, Muroran, Japan, 2008, pp.258–263. USA: IEEE.

10.

Horn

Schunck

. Determining optical flow. Artif Intell 1981; 17: 185–203.

11.

Beauchemin

Barron

. The computation of optical flow. ACM Comput Surv New York, USA 1995; 2: 433–467.

12.

Young GS, Hong TH, Herman M, et al. Obstacle detection for a vehicle using optical flow. In: Proceedings of the intelligent vehicles '92 symposium (Cat. No.92TH0468-9), Detroit, MI USA, 1992, pp.185–190.

13.

Mikawa

Yoshida

Tanno

. Vision-based redundancy control of robot manipulators for obstacle avoidance. IECON Proc Ind Electron Conf 1997; 3: 1373–1378.

14.

Lenser

Veloso

. Visual sonar: Fast obstacle avoidance using monocular vision. IEEE Int Conf Intell Robots Syst 2003; 1: 886–891.

15.

Hatsopoulos N and Anderson J. Collision-avoidance system based on optical flow. In: Proceedings of the intelligent vehicles '92 symposium, Cat. No.92TH0468-9, Detroit, MI, USA, 1992, pp.79–84.

16.

Barrows G. Mixed mode VLSI optic flow sensor for micro air vehicles. PhD Dissertation, University of Maryland, 1999.

17.

Special issue on segmentation. IEEE Trans Circuits Syst Video Technol 1998; September 8: 5.

18.

Phillips D. Image processing in C. Lawrence, Kansas, USA: R&D Publications, 1994.

19.

Sezan

. A peak detection algorithm and its application to histogram-based image data reduction. Comput Vis Graph Image Process 1990; 49: 36–51.

20.

MPEG-1. Wikipedia, the free encyclopedia. http://en.wikipedia.org/wiki/MPEG-1 (accessed October 2009).

21.

Ghanbari

. Standard codecs—Image compression to advanced video coding, London: The Institution of Engineering and Technology, 2008.

22.

Ebrahimi

. MPEG-4 video verification model: A video encoding/decoding algorithm based on content representation. Signal Process Image Commun 1997; 9: 367–384.

23.

Liou

. A simple and efficient search algorithm for block-matching motion estimation. IEEE Trans Circuits Syst Video Technol 1997; 7(2): 429–433.

24.

. A novel four-step search algorithm for fast block motion estimation. IEEE Trans Circuits Syst Video Technol 1996; 6(3): 313–317.

25.

Zhu

. A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans Image Process 2000; 9(2): 287–290.

26.

Nie

. Adaptive rood pattern search algorithm for fast block-matching motion estimation. IEEE Trans Image Process 2002; 11(12): 1442–1448.

27.

Huang

Tsai

. A simple and efficient block motion estimation algorithm based on full-search array architecture. Signal Process Image Commun 2004; 19: 975–992.

28.

Rehan

El-Kharashi

Gebali

. A hierarchical design methodology for full-search block matching motion estimation. Multidim Syst Sign Process 2006; 17: 327–341.

29.

Lim C, Kang H, Kim T, et al. A fast full search algorithm for variable block-based motion estimation of H.264. In: Advances in visual computing-first international symposium, ISVC v 3804 LNCS, 2005, pp.710–717. Berlin, Heidelberg, Germany: Springer-Verlag.

30.

Dung D and Luo W. Hybrid vector filtering for noise detection and removal in color images. In: 24th international conference on computers and their applications, CATA2009, New Orleans, Louisiana, USA, 2009, pp.19–24. USA: ISCA.

31.

Said A. White and color noise cancellation using adaptive feedback cross-coupled line enhancer filter. In: Proceedings of the 8th IEEE international symposium on signal processing and information technology, ISSPIT 2008, Sarajevo, Bosnia and Herzegovina, 2008, pp.207–212. USA: IEEE.

32.

Phanindra

Sirish

. An image differencing method for interface level detection in separation cells. Mach Vis Appl 2012; 23: 283–298.

33.

http://www.technologystudent.com/designpro/pricol1.htm (accessed 12 September 2013).

34.

http://www.physicsclassroom.com/class/light/u12l2e.cfm (accessed 12 September 2013).

35.

http://en.wikipedia.org/wiki/Color (accessed 12 September 2013).

36.

http://www.societyofrobots.com/sensors_color.shtml (accessed 12 September 2013).

37.

E-Sky E020 “Big Lama” product website: http://www.twf-sz.com/english/products.asp?prodid=0291 (accessed February 2010).

38.

Chowdhary G, Ottander J, Salaun E, et al. Low cost guidance, navigation, and control solutions for miniature air vehicle in GPS denied environments. In: First symposium on indoor flight, international aerial robotics competition, Mayaguez, Puerto Rico, 2009. USA: AuVSI.

39.

Gonzalez

Woods

Eddins

. Digital image processing using Matlab, 2nd ed. Tennessee USA: Gatesmark Publishing Knoxville, 2009.

40.

Gonzalez

Woods

. Digital image processing 20022nd ed. New Jersey USA: Prentice Hall.