UAV Guidance: A Stereo-Based Technique for Interception of Stationary or Moving Targets

  • Reuben StrydomEmail author
  • Saul Thurrowgood
  • Aymeric Denuelle
  • Mandyam V. Srinivasan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9287)


We present a novel stereo-based method for the interception of a static or moving target, from an Unmanned Aerial Vehicle (UAV). This technique is directly applicable for outdoor applications such as search and rescue, monitoring and surveillance, and complex landing scenarios. Stereo vision is particularly useful for the interception of a moving target as it intrinsically measures the relative position and velocity between the UAV and the person or object. The target position is computed geometrically using its direction, as viewed by the vision system, and the UAV’s stereo height. A Kalman filter computes a reliable estimate of relative position and velocity using the target centroid. The performance of this method is validated by conducting a number of closed-loop interceptions for both static and moving target cases. The mean interception error is found to be 0.01m with a standard deviation of 0.33m in tests with static targets and 0.14m with a standard deviation of 0.24m in tests with moving targets. Our method has been field-tested outdoors and provides results comparable to other vision-based techniques that have been tested under more controlled indoor conditions.


Static Target Unmanned Aerial Vehicle Stereo Vision Velocity Error Angular Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6), 641–647Google Scholar
  2. 2.
    Azrad, S., Kendoul, F., Nonami, K.: Visual servoing of quadrotor micro-air vehicle using color-based tracking algorithm. Journal of System Design and Dynamics 4(2), 255–268, 1881–3046 (2010)Google Scholar
  3. 3.
    Choi, J.H., Lee, D., Bang, H.: Tracking an unknown moving target from uav: extracting and localizing an moving target with vision sensor based on optical flow. In: 2011 5th International Conference on Automation, Robotics and Applications (ICARA), pp. 384–389. IEEE (2011)Google Scholar
  4. 4.
    Garratt, M., Pota, H., Lambert, A., Eckersley-Masline, S., Farabet, C.: Visual tracking and lidar relative positioning for automated launch and recovery of an unmanned rotorcraft from ships at sea. Naval Engineers Journal 121(2), 99–110Google Scholar
  5. 5.
    Gomez-Balderas, J.-E., Flores, G., García Carrillo, L.R., Lozano, R.: Tracking a ground moving target with a quadrotor using switching control. Journal of Intelligent & Robotic Systems 70(1–4), 65–78, 0921–0296 (2013)Google Scholar
  6. 6.
    Guenard, N., Hamel, T., Mahony, R.: A practical visual servo control for an unmanned aerial vehicle. IEEE Transactions on Robotics 24(2), 331–340 (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    He, W., Fang, Y., Zhang, X.: Prediction-based interception control strategy design with a specified approach angle constraint for wheeled service robots. IEEE (2013)Google Scholar
  8. 8.
    Kannala, J., Brandt, S.S.: A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(8), 1335–1340 (2006)CrossRefGoogle Scholar
  9. 9.
    Lange, S., Sunderhauf, N., Protzel, P.: A vision based onboard approach for landing and position control of an autonomous multirotor uav in gps-denied environments. In: International Conference on Advanced Robotics, ICAR 2009, pp. 1–6. IEEE (2009)Google Scholar
  10. 10.
    Li, W., Zhang, T., Kuhnlenz, K.: A vision-guided autonomous quadrotor in an air-ground multi-robot system. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 2980–2985. IEEE (2011)Google Scholar
  11. 11.
    Low, E.M.P., Manchester, I.R., Savkin, A.V.: A biologically inspired method for vision-based docking of wheeled mobile robots. Robotics and Autonomous Systems 55(10), 769–784 (2007)CrossRefGoogle Scholar
  12. 12.
    Moore, R.J.D: Vision systems for autonomous aircraft guidance. PhD thesis, The University of Queensland (2012)Google Scholar
  13. 13.
    Salazar-Cruz, S., Escareno, J., Lara, D., Lozano, R.: Embedded control system for a four-rotor uav. International Journal of Adaptive Control and Signal Processing 21(2–3), 189–204Google Scholar
  14. 14.
    Sanchez-Lopez, J.L., Pestana, J., Saripalli, S., Campoy, P.: An approach toward visual autonomous ship board landing of a vtol uav. Journal of Intelligent & Robotic Systems 74(1–2), 113–127, 0921–0296 (2014)Google Scholar
  15. 15.
    Strydom, R., Thurrowgood, S., Srinivasan, M.V.: Visual odometry: autonomous uav navigation using optic flow and stereo. In: Australasian Conference on Robotics and Automation (ACRA), pp. 1–10. Australian Robotics and Automation Association (2014)Google Scholar
  16. 16.
    Thurrowgood, S., Moore, R.J.D., Soccol, D., Knight, M., Srinivasan, M.V.: A biologically inspired, vision-based guidance system for automatic landing of a fixed-wing aircraft. Journal of Field Robotics 31(4), 699–727 (2014)CrossRefGoogle Scholar
  17. 17.
    Watanabe, Y., Lesire, C., Piquereau, A., Fabiani, P., Sanfourche, M., Besnerais, G.L.: The onera ressac unmanned autonomous helicopter: visual air-to-ground target tracking in an urban environment. In: American Helicopter Society 66th Annual Forum (AHS 2010) (2010)Google Scholar
  18. 18.
    Wenzel, K.E., Rosset, P., Zell, A.: Low-cost visual tracking of a landing place and hovering flight control with a microcontroller. In: 2nd International Symposium on Selected Papers From the UAVs, Reno, Nevada, USA June 8–10, 2009, pp. 297–311. Springer (2010)Google Scholar
  19. 19.
    Yang, X., Mejias, L., Garratt, M.: Multi-sensor data fusion for UAV navigation during landing operations. Australian Robotics and Automation Association Inc., Monash University (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Reuben Strydom
    • 1
    Email author
  • Saul Thurrowgood
    • 1
  • Aymeric Denuelle
    • 1
  • Mandyam V. Srinivasan
    • 1
  1. 1.The University of QueenslandBrisbaneAustralia

Personalised recommendations