Sensitivity Study of Fiducial-Aided Navigation of Unmanned Aerial Vehicles
This work addresses reliable navigation for UAVs in urban environments, but it is incremental as it builds on existing EKF and fiducial methods.
This research tackled the problem of precise UAV navigation in urban areas where GNSS signals are degraded by developing an EKF that combines IMU and camera-based fiducial measurements, achieving accurate state estimation with sensitivity analysis on parameters like IMU grade and fiducial placement.
The possible applications and benefits of autonomous Unmanned Aerial Vehicle (UAV) use in urban areas are gaining considerable attention. Before these possibilities can be realized, it is essential that UAVs be able to navigate reliably and precisely in urban environments. The most common means of determining the location of a UAV is to utilize position measurements from Global Navigation Satellite Systems (GNSS). In urban environments, however, GNSS measurements are significantly degraded due to occlusions and multipath. This research analyzes the use of camera Line-of-Sight (LOS) measurements to self-describing fiducials as a replacement for conventional GNSS measurements. An extended Kalman filter (EKF) is developed and validated for the purpose of combining continuous measurements from an Inertial Measurement Unit (IMU) with the discrete LOS measurements to accurately estimate the states of a UAV. The sensitivity of the estimation error covariance to various system parameters is assessed, including IMU grade, fiducial placement, vehicle altitude, and image processing frequency.