Integration of image de-blurring in an aerial Mono-SLAM

Abstract

In this article, we discuss the possibility of integrating image de-blurring techniques in an aerial simultaneous localization and mapping by a single camera (monocular simultaneous localization and mapping (Mono-SLAM)). We use an integrated aerial virtual environment together with a six-degree-of-freedom aircraft flight simulator to show the effectiveness of the approach to generate three-dimensional flight trajectories via integration of image de-blurring in the associated loop of the Mono-SLAM. The objective is to increase the overall performance of a flying mission over an unknown area by means of a vision-only method. The integrated aerial virtual environment produces and collects real-time pictures from a nadir-looking vision sensor mounted on the vehicle. Our MATLAB GUI-based toolbox helps user to investigate an offline Mono-SLAM with a predefined de-blurring method integrated with an estimator which extracts navigational parameters. The system efficient architecture allows effective virtual experiments in a completely unknown environment, without using preloaded maps or predefined features. Simulation outcomes demonstrate the feasibility of navigation of aerial robots in inaccessible environments. Different case studies support the conclusion; nonetheless, we observe a number of nonlinearities from de-blurring filters even for a general aviation aircraft.

Keywords

Aerial navigation image de-blurring de-blurring in-the-loop SLAM monocular SLAM flight simulation virtual reality

Get full access to this article

View all access options for this article.

References

Smith

Cheeseman

. On the representation of spatial uncertainty. Int J Robot Res 1987; 5(4): 56–68.

Kim J, Ong S, Nettleton E, et al. Decentralized approach to UAV navigation: without the use of GPS and preloaded maps. 2006.

Brooks

. What's real about virtual reality, Chapel Hill: University Of North Carolina, 1999.

Amiri Atashgah

Malaek

SMB

. An integrated virtual environment for feasibility studies and implementation of aerial MonoSLAM. Virtual Reality 2012; 16(3): 215–232.

Amiri Atashgah

Malaek

SMB

. Prediction of aerial-image motion blurs due to the flying vehicle dynamics and camera characteristics in a virtual environment. Proc IMechE, Part G: J Aerospace Engineering 2012. Epub ahead of printDOI: 10.1177/0954410012450107.

Amiri Atashgah

Malaek

SMB

. A simulation environment for path and image generation in an aerial single-camera vision system. Proc IMechE, Part G: J Aerospace Engineering 2011; 225(5): 541–558.

Kim JH and Sukkarieh S. Airborne simultaneous localization and map building. In: IEEE international conference robotics and automation, Taipei, Taiwan, 14–19 September 2003, pp.406–411.

Jung I and Lacroix S. High resolution terrain mapping using low altitude aerial stereo imagery. In: Ninth international conference computer vision, Nice, France, 14–17 October 2003.

Davison

Reid

Molton

. MonoSLAM: real-time single camera SLAM. IEEE Trans Pattern Anal Mach Intell 2007; 29(6): 1052–1067.

10.

Civera J, Montiel JM and Davison AJ. Inverse depth parametrization for monocular SLAM. IEEE Trans Robot 2008; 24(5): 932–945.

11.

Johnson

Proctor

Tannenbaum

. Visual search automation for unmanned aerial vehicles. IEEE Trans Aerosp Electr Syst 2005; 41(1): 219–232.

12.

Cesetti A, Frontoni E, Mancini A, et al. Vision-based autonomous navigation and landing of an unmanned aerial vehicle using natural landmarks. In: 17th Mediterranean conference on control & automation, Makedonia Palace, Thessaloniki, Greece, 2009, pp.910–915.

13.

Funke J and Informatik F. A framework for evaluating visual SLAM. In: British machine vision conference, 2009 [Online], www.bmva.org/bmvc/2009/Papers/Paper396/Paper396.pdf (2009, accessed 2011).

14.

Johnson AE, Ansar A, Matthies LH, et al. A general approach to terrain relative navigation for planetary landing. In: AIAA conference and exhibit, Rohnert Park, CA, USA, 7–10 May 2007.

15.

Mourikis AI, Trawny N, Roumeliotis SI, et al. Vision-aided inertial navigation for precise planetary landing: analysis and experiments. In: AIAA conference paper, Atlanta, GA, 26–30 June 2007.

16.

Peleg

Rav-Acha

. Two motion-blurred images are better than one. Pattern Recog Lett 2005; 26: 311–317.

17.

Liu

Gamal

. Synthesis of high dynamic range motion blur free image from multiple captures. IEEE Trans Circuits Syst 2003; 50(4): 530–539.

18.

Ben-Ezra

Nayar

. Motion-based motion deblurring. IEEE Trans Pattern Anal Mach Intell 2004; 26(6): 689–698.

19.

Jones

. Aerial inspection of overhead power lines using video: estimation of image blurring due to vehicle and camera motion. IEE Proc Vis Image Signal Process 2000; 147(2): 157–166.

20.

Woods

Gonzales

. Digital image processing using MATLAB, 2nd ed. New Jersey: Prentice-Hall, 2002.

21.

Woods

Gonzalez

. Digital image processing, 3rd ed. New Jersey: Pearson Education, 2008.

22.

Chan

Wong

C-K

. Total variation blind deconvolution. IEEE Trans Image Process 1998; 3(3): 370–375.

23.

You

Y-L

Kaveh

. A regularization approach to joint blur identification and image restoration. IEEE Trans Image Process 1996; 5(3): 416–428.

24.

Tiwary

Kim

Singh

. An adaptively accelerated Lucy-Richardson method for image deblurring. EURASIP J Adv Signal Process 2008; 8: 110–110.

25.

Richardson

. Bayesian-based iterative method of image restoration. J Opt Soc Am 1972; 62(1): 55–59.

26.

Lucy

. An iterative technique for the rectification of observed distortions. Astron J 1974; 79.

27.

Qiu P and Hall P. Blind deconvolution and deblurring in image analysis. Stat Sin 2007; 17: 1483–1509.

28.

Davison AJ, Cid YG and Kita N. Real-time 3D SLAM with wide-angle vision. In: IFAC symposium on intelligent autonomous vehicles, Instituto Superior Técnico, Lisboa, Portugal, 5–7 July 2004.

29.

Blanco JL. The mobile robot programming toolkit [Online], http://babel.isa.uma.es/mrpt/index.php/Main_Page, 2010 (2010, accessed 2010).

30.

Sola J. Towards visual localization, mapping and moving objects tracking by a mobile robot: a geometric and probabilistic approach. PhD Thesis, National Polytechnic Institute of Toulouse, France, 2007.

31.

Stevens

Lewis

. Aircraft control and simulation, New York: Wiley, 2003.

32.

Montiel JMM, Civera J and Davison AJ. Unified inverse depth parametrization for monocular SLAM. In: Robotics: science and systems, Philadelphia, USA, August 2006.

33.

JSBSim Project [Online]. www.jsbsim.org (accessed 2010).