Advertisement

Indoor Exploration Using a μUAV and a Spherical Geometry Based Visual System

  • Tiago Caldeira
  • Lakmal Seneviratne
  • Jorge Dias
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 394)

Abstract

This research presents a new vision system that explores a spherical geometry and will be provide innovative solutions for tracking, surveillance, navigation and mapping with micro Unmanned Aerial Vehicle (μUAV) in unknown indoor environments. The system will be used with μUAV in indoor environment and it is composed by twenty six cameras that are arranged in order to sample different parts of the visual sphere around the μUAV. This configuration allows that some of the cameras will have overlapped field of view. This system has been designed for the purpose of recovering ego-motion and structure from multiple video images, having a distributed omnidirectional field of view. We use the spherical geometry to extend the field of view, from one single direction to a single point of perspective, but with multiple views. This manuscript will prove that spherical geometric configuration has advantages when compared to stereo cameras for the estimation of the system’s own motion and consequently the estimation of shape models from each camera. The preliminary field tests presented the theoretical potential of this system and the experimental results with the images acquired by 3 cameras.

Keywords

Vision μUAV Spherical Geometry Indoor Ego-motion Trifocal tensor Motion Flow 

References

  1. 1.
    Morris, W., Dryanovski, I., Xiao, J.: 3D Indoor Mapping for Micro-UAVs Using Hybrid Range Finders and Multi-Volume Occupancy Grids. In: Proc. of Robotics: Science and Systems, RSS (2010)Google Scholar
  2. 2.
    Ahrens, S., Levine, D., Andrews, G., How, J.P.: Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied Environments. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2643–2648 (2009)Google Scholar
  3. 3.
    Achtelik, M., Bachrach, A., He, R., Prentice, S., Roy, N.: Stereo Vision and Laser Odometry for Autonomous Helicopters in GPS-Denied Indoor Environments. In: Proceedings of the SPIE Conference on Unmanned Systems Technology XI, Orlando, FL (2009)Google Scholar
  4. 4.
    Lobo, J.: Integration of Vision and Inertial Sensing. PhD Thesis, U. of Coimbra (2006)Google Scholar
  5. 5.
    Corke, P., Lobo, J., Dias, J.: An introduction to inertial and visual sensing. International Journal of Robotics Research, Special Issue 2nd Workshop on Integration of Vision and Inertial Sensors 26(6), 519–535 (2007)Google Scholar
  6. 6.
    Lobo, J., Dias, J.: Relative Pose Calibration Between Visual and Inertial Sensors. International Journal of Robotics Research, Special Issue 2nd Workshop on Integration of Vision and Inertial Sensors 26(6), 561–575 (2007)Google Scholar
  7. 7.
    Dias, J., Araújo, H., Paredes, C., Batista, J.: Optical Normal Flow Estimation on Log-polar Images. A solution for Real-Time Binocular Vision. Real-Time Imaging Journal 3, 213–228 (1997)CrossRefGoogle Scholar
  8. 8.
    Ashton, K.: That ’Internet of Things’ Thing. RFID Journal (July 22, 2009)Google Scholar
  9. 9.
    Bouabdallah, S., Siegwart, R.: Full Control of a Quadrotor. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS (2007)Google Scholar
  10. 10.
    Michael Sobers Jr., D., Chowdhary, G., Johnson, E.N.: Indoor Navigation for Unmanned Aerial Vehicles. In: AIAA Guidance, Navigation, and Control Conference (2009)Google Scholar
  11. 11.
    Achtelika, M., Bachrach, A., He, R., Prentice, S., Roy, N.: Autonomous navigation and exploration of a quadrotor helicopter in GPS-denied indoor environments. In: Robotics: Science and Systems (2008)Google Scholar
  12. 12.
    Chowdhary, G., Michael Sobers Jr., D.: Integrated Guidance Navigation and Control for a Fully Autonomous Indoor UAS, Portland, Oregon (2011)Google Scholar
  13. 13.
    Morris, W., Dryanovski, I., Xiao, J.: 3D Indoor Mapping for Micro-UAVs Using Hybrid Range Finders and Multi-Volume Occupancy Grids. In: Proceedings of Robotics: Science and Systems, RSS (2010)Google Scholar
  14. 14.
    Ahrens, S., Levine, D., Andrews, G., How, J.P.: Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied environments. In: Proceedings IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 2643–2648 (2009)Google Scholar
  15. 15.
    Grzonka, S., Grisetti, G., Burgard, W.: A fully autonomous indoor quadrotor. IEEE Transactions on Robotics (99), 1–11 (2012)Google Scholar
  16. 16.
    Bachrach, A., He, R., Roy, N.: Autonomous Flight in Unknown Indoor Environments. Intl. Journal of Micro Air Vehicles, 217–228 (2009)Google Scholar
  17. 17.
    Blöesch, M., Weiss, S., Scaramuzza, D., Siegwart, R.: Vision Based MAV Navigation in Unknown and Unstructured Environments. In: IEEE International Conference on Robotics and Automation (2010)Google Scholar
  18. 18.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (March 2004)Google Scholar
  19. 19.
    Hartley, R.I.: Lines and points in three views and the trifocal tensor. International Journal of Computer Vision 22(2), 125–140 (1996)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Tiago Caldeira
    • 1
  • Lakmal Seneviratne
    • 1
    • 2
  • Jorge Dias
    • 1
    • 3
  1. 1.Khalifa University for Science Technology and ResearchUAE
  2. 2.Center for Robotics Research from Kings College of LondonUK
  3. 3.Institute of Systems and RoboticsUniversity of CoimbraPortugal

Personalised recommendations