ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-2/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 187–194, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-187-2019
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 187–194, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-187-2019

  29 May 2019

29 May 2019

ENHANCED UAV NAVIGATION USING HALL-MAGNETIC AND AIR-MASS FLOW SENSORS IN INDOOR ENVIRONMENT

S. Zahran1, A. Moussa1,2, and N. El-Sheimy1 S. Zahran et al.
  • 1Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada
  • 2Department of Electrical Engineering, Port-Said University, Port Said, Egypt

Keywords: Hall-Effect Sensor, Mass-Flow Sensor, Extended Kalman Filter, Odometer, Sensors Fusion

Abstract. The use of Unmanned Aerial Vehicles (UAVs) in many commercial and emergency applications has the potential to dramatically alter several industries, and, in the process, change our attitudes regarding their impact on our daily lives activities. The navigation system of these UAVs mainly depends on the integration between the Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) to estimate the positions, velocities, and attitudes (PVT) of the UAVs. However, GNSS signals are not always available everywhere and therefore during GNSS signal outages, the navigation system performance will deteriorate rapidly especially when using low-cost INS. Additional aiding sensors are required, during GNSS signal outages, to bound the INS errors and enhance the navigation system performance. This paper proposes the utilization of two sensors (Hall-magnetic and Air-Mass flow sensors) to act as flying odometer by estimating the UAV forward velocity. The estimated velocity is then integrated with INS through Extended Kalman Filter (EKF) to enhance the navigation solution estimation. A real experiment was carried out with the 3DR quadcopter while the proposed system is attached on the top of the quadcopter. The results showed great enhancement in the navigation system performance with more than 98% improvement when compared to the free running INS solution (dead-reckoning).