Quadrotor UAV Indoor Localization Using Embedded Stereo Camera

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Localization of Small-Size Unmanned Air Vehicles (UAVs) such as the Quadrotors inGlobal Positioning System (GPS)-denied environment such as indoors has been done using varioustechniques. Most of the experiment indoors that requires localization of UAVs, used cameras or ultrasonicsensors installed indoor or applied indoor environment modification such as patching (InfraRed) IR and visual markers. While these systems have high accuracy for the UAV localization, theyare expensive and have less practicality in real situations. We propose a system consisting of a stereocamera embedded on a quadrotor UAV for indoor localization. The optical flow data from the stereocamera then are fused with attitude and acceleration data from our sensors to get better estimationof the quadrotor location. Using stereo camera capabilities the quadrotor altitude are estimated usingSIFT Feature Stereo Matching are used in addition to the altitude estimation computed using opticalflow. To avoid latency due to computational time, image processing and the quadrotor control areprocessed threads and core allocation.

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Periodical:

Edited by:

R. Varatharajoo, F.I. Romli, K.A. Ahmad, D.L. Majid and F. Mustapha

Pages:

270-277

Citation:

S. Azrad et al., "Quadrotor UAV Indoor Localization Using Embedded Stereo Camera", Applied Mechanics and Materials, Vol. 629, pp. 270-277, 2014

Online since:

October 2014

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[1] G. Barrows, C. Neely, and K. Miller. Optic flow ssensors for mav navigation, in fixed and flapping wing aerodynamics for micro air vehicle applications. In Progress in Astronautics and Aeronautics, AIAA, volume 195, pages 557-554, (2001).

DOI: https://doi.org/10.2514/5.9781600866654.0557.0574

[2] A. Beyeler, C. Mattiussi, J.C. Zufferey, and D. Floreano. Vision-based altitude and pitch estimation for ultra-light indoor microflyers. In Proceedings of IEEE International Conference on Robotics and Automation, ICRA, (2006).

DOI: https://doi.org/10.1109/robot.2006.1642131

[3] J. Chahl, M. Srinivasan, and H. Zhang. Landing strategies in honeybees and applications to uninhabited airborne vehicles. The International Journal of Robotics Research, 23(2): 101-110, (2004).

DOI: https://doi.org/10.1177/0278364904041320

[4] William E. Green, Paul Y. Oh, Keith Sevcik, and Geoffrey Barrows. Autonomous landing for indoor flying robots using optic flow. In in ASME International Mechanical Engineering Congress and Exposition, pages 1347-1352, (2003).

DOI: https://doi.org/10.1115/imece2003-55424

[5] Pedro J. Garcia-Pardo, Gaurav S. Sukhatme, and James F. Montgomery. Towards vision-based safe landing for an autonomous helicopter. Robotics and Autonomous Systems, pages 19-29, (2002).

DOI: https://doi.org/10.1016/s0921-8890(01)00166-x

[6] Courtney S. Sharp, Omid Shakernia, and Shankar Sastry. A vision system for landing an unmanned aerial vehicle. In Proceedings of IEEE International Conference on Robotics and Automation, ICRA, pages 1720-1727. IEEE, (2001).

DOI: https://doi.org/10.1109/robot.2001.932859

[7] Srikanth Saripalli, James F. Montgomery, and Gaurav S. Sukhatme. Visually-guided landing of an unmanned aerial vehicle. IEEE Transactions on Robotics and Automation, 19: 371-381, (2003).

DOI: https://doi.org/10.1109/tra.2003.810239

[8] Il-Kyun Jung and Simon Lacroix. High resolution terrain mapping using low altitude aerial stereo imagery. In 9th IEEE International Conference on Computer Vision (ICCV 2003), 14-17 October 2003, Nice, France, pages 946-951. IEEE Computer Society, (2003).

DOI: https://doi.org/10.1109/iccv.2003.1238450

[9] M. Sanfourche, G. Le Besnerais, and S. Philipp-Foliguet. Height estimation using aerial side looking image sequences. In Proceedings of ISPRS Conference PIA'03 (Photogrammetric Image Analysis), volume vol. XXXIV, M"̆nchen, sept 2003. ISPRS.

[10] Damien Eynard, Pascal Vasseur, Cédric Demonceaux, and Vincent Frémont. Uav altitude estimation by mixed stereoscopic vision. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 18-22, 2010, Taipei, Taiwan.

DOI: https://doi.org/10.1109/iros.2010.5652254

[11] Farid Kendoul, Isabelle Fantoni, and Kenzo Nonami. Optic flow-based vision system for autonomous 3d localization and control of small aerial vehicles. Robotics and Autonomous Systems, 57(6-7): 591-602, (2009).

DOI: https://doi.org/10.1016/j.robot.2009.02.001

[12] Changchang Wu. Siftgpu: A gpu implementation of scale invariant feature transform (sift). 2007. http: /cs. unc. edu/~ccwu/siftgpu/ Accessed on September (2011).

[13] Martin A. Fischler and Robert C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6): 381-395, (1981).

DOI: https://doi.org/10.1145/358669.358692