Experimentally validated extended Kalman filter for UAV state estimation using low-cost sensors

S.P.H. Driessen, N.H.J. Janssen, L. Wang, J.L. Palmer, H. Nijmeijer

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)


Visually based velocity and position estimations are often used to reduce or remove the dependency of an unmanned aerial vehicle (UAV) on global navigation satellite system signals, which may be unreliable in urban canyons and are unavailable indoors. In this paper, a sensor-fusion algorithm based on an extended Kalman filter is developed for the velocity, position, and attitude estimation of a UAV using low-cost sensors. In particular an inertial measurement unit (IMU) and an optical-flow sensor that includes a sonar module and an additional gyroscope are used. The algorithm is shown experimentally to be able to handle measurements with different sampling rates and missing data, caused by the indoor, low-light conditions. State estimations are compared to a ground-truth pose history obtained with a motion-capture system to show the influence of the optical-flow and sonar measurements on its performance. Additionally, the experimental results demonstrate that the velocity and attitude can be estimated without drift, despite the magnetic distortions typical of indoor environments.

Original languageEnglish
Pages (from-to)43-48
Number of pages6
Issue number15
Publication statusPublished - 1 Jan 2018
Event18th IFAC Symposium on System Identification (SYSID 2018) - Stockholm, Sweden
Duration: 9 Jul 201811 Jul 2018


  • Extended Kalman filter
  • Missing data
  • Multi-rate sampled data
  • Optical flow
  • Sensor fusion
  • Unmanned aerial vehicle
  • Visual-inertial state estimation


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