Abstract
Abstract. There is an increasingly large number of applications for Unmanned Aerial Vehicles (UAVs) from monitoring, mapping and target geolocation. However, most of commercial UAVs are equipped with low-cost navigation sensors such as C/A code GPS and a low-cost IMU on board, allowing a positioning accuracy of 5 to 10 meters. This low accuracy cannot be used in applications that require high precision data on cm-level. This paper presents a precise process for geolocation of ground targets based on thermal video imagery acquired by small UAV equipped with RTK GPS. The geolocation data is filtered using an extended Kalman filter, which provides a smoothed estimate of target location and target velocity. The accurate geo-locating of targets during image acquisition is conducted via traditional photogrammetric bundle adjustment equations using accurate exterior parameters achieved by on board IMU and RTK GPS sensors, Kalman filtering and interior orientation parameters of thermal camera from pre-flight laboratory calibration process. The results of this study compared with code-based ordinary GPS, indicate that RTK observation with proposed method shows more than 10 times improvement of accuracy in target geolocation.
Highlights
Achieving accurate position deals great challenges when commercial small unmanned platforms are used
The Unmanned Aerial Vehicles (UAVs) position is given by an on-board Global Positioning Systems (GPS) receiver, while the attitude angles are computed from a navigation filter which integrates the inertial sensors and the GPS
Simulation results are presented for two scenarios in which different target speeds are considered. (Shukla, 2014) utilized automatic feature based registration on technique of a georeferenced satellite image with an aerial image which is already stored in UAV’s database to retrieve the geolocation of the target
Summary
Achieving accurate position deals great challenges when commercial small unmanned platforms are used. The UAV position is given by an on-board GPS receiver, while the attitude angles are computed from a navigation filter which integrates the inertial sensors (gyroscopes, magnetometer and accelerometers) and the GPS (Barton 2012) By using this approach to solve the localization problem, both lateral and vertical positioning errors of the GPS receivers will contribute to the sources of error for target’s location estimation. They apply recursive least square filtering to the image sequence and account for navigation biases and wind to improve accuracy up to 3m with no differential GPS They explored the problem of flight path optimization by finding an optimal altitude and radius for a circular trajectory above the stationary target. Simulation results are presented for two scenarios in which different target speeds are considered. (Shukla, 2014) utilized automatic feature based registration on technique of a georeferenced satellite image with an aerial image which is already stored in UAV’s database to retrieve the geolocation of the target
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