Delay and Dropout Tolerant State Estimation for MAVs

  • Frédéric BourgeoisEmail author
  • Laurent Kneip
  • Stephan Weiss
  • Roland Siegwart
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)


This paper presents a filter based position and velocity estimation for an aerial vehicle fusing inertial and delayed, dropout-susceptible vision measurements, without the a priori knowledge of the exact variable time delay. The data from the two sensors, which are running at different rates, is transmitted via independent wireless links to a ground station. A synchronization between both communication ways makes it possible to determine the image transmission and processing time. The computational complexity of the algorithm is kept at a low level. The images are processed by a Visual SLAM algorithm that builds up a map of the area and simultaneously tracks the pose of the camera. With a delay going up to 230 ms and an amount of 16% dropout in the vision data, we show that with the presented filter a quadrotor can be stabilized and kept in the region of a setpoint with a simple PID controller.


Global Position System Unmanned Aerial Vehicle Inertial Measurement Unit Acceleration Measurement Camera Frame 
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.


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  1. 1.
    Abdelkrim, N., Aouf, N., Tsourdos, A., White, B.: Robust nonlinear filtering for INS/GPS UAV localization. In: Proceedings of the 16th Mediterranean Conference on Control and Automation, pp. 695–702. IEEE Control Systems Society, Ajaccio (2008)Google Scholar
  2. 2.
    Armesto, L., Chroust, S., Vincze, M., Tornero, J.: Multi-rate fusion with vision and inertial sensors. In: Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, LA, USA, pp. 193–199 (2004)Google Scholar
  3. 3.
    AscTec Hummingbird Quadrotor Helicopter, Ascending Technologies GmbH,
  4. 4.
    Blösch, M., Weiss, S., Scaramuzza, D., Siegwart, S.: Vision Based MAV Navigation in Unknown and Unstructured Environments. In: Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA (2010)Google Scholar
  5. 5.
    Bryson, M., Sukkarieh, S.: Vehicle model aided inertial navigation for a UAV using low-cost sensors. In: Proceedings of the Australasian Conference on Robotics and Automation. Australian Robotics and Automation Association, Canberra, Australia (2004)Google Scholar
  6. 6.
    Cheviron, T., Hamel, T., Mahony, R.E., Baldwin, G.: Robust nonlinear fusion of inertial and visual data for position, velocity and attitude estimation of UAV. In: Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy, pp. 2010–2016 (2007)Google Scholar
  7. 7.
    Hsiao, F.H., Pan, S.-T.: Robust Kalman filter synthesis for uncertain multiple time-delay stochastic systems. Journal of Dynamic Systems, Measurement, and Control 118(4), 803–808 (1996)zbMATHCrossRefGoogle Scholar
  8. 8.
    Kingston, D.B., Beard, A.W.: Real-time Attitude and Position Estimation for Small UAVs Using Low-Cost Sensors. In: Proceedings of the AIAA 3rd Unmanned Unlimited Technical Conference, Workshop and Exhibit, Chicago, Illinois, USA, pp. 2004–6488 (2004)Google Scholar
  9. 9.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 1–10. IEEE Computer Society, Washington, DC (2007)CrossRefGoogle Scholar
  10. 10.
    Larsen, T.D., Andersen, N.A., Ravn, O., Poulsen, N.K.: Incorporation of time delayed measurements in a discrete-time Kalman filter. In: Proceedings of the 37th IEEE Conference on Decision & Control, Tampa, Florida, USA, pp. 3972–3977 (1998)Google Scholar
  11. 11.
    Lobo, J., Dias, J.: Integration of inertial information with vision towards robot autonomy. In: Proceedings of the IEEE International Symposium on Industrial Electronics, Guimaraes, Portugal, pp. 825–830 (1997)Google Scholar
  12. 12.
    Nützi, G., Weiss, S., Scaramuzza, D., Siegwart, R.: Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM. In: International Conference on Unmanned Aerial Vehicles, Dubai (2010)Google Scholar
  13. 13.
    Rehbinder, H., Ghosh, B.K.: Pose estimation using line-based dynamic vision and inertial sensors. IEEE Transactions on Automatic Control 48(2), 186–199 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Sukkarieh, S., Nebot, E.M., Durrant-Whyte, H.F.: A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Transactions on Robotics and Automation 15(3), 572–578 (1999)CrossRefGoogle Scholar
  15. 15.
    van der Merwe, R., Wan, E., Julier, S.: Sigma-point Kalman filters for nonlinear estimation and sensor-fusion: Applications to integrated navigation. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, Providence, Rhode Island (2004)Google Scholar
  16. 16.
    Yun, B., Peng, K., Chen, B.: Enhancement of GPS signals for automatic control of a UAV helicopter system. In: Proceedings of the IEEE International Conference on Control and Automation, IAENG, Hong Kong, pp. 1185–1189 (2007)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Frédéric Bourgeois
    • 1
    Email author
  • Laurent Kneip
    • 1
  • Stephan Weiss
    • 1
  • Roland Siegwart
    • 1
  1. 1.Autonomous Systems LabETH ZurichZurichSwitzerland

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