Abstract
This paper presents an augmented reality-based method for geo-registering videos from low-cost multi-rotor Unmanned Aerial Vehicles (UAVs). The goal of the proposed method is to conduct an accurate geo-registration and target localization on a UAV video stream. The geo-registration of video stream requires accurate attitude data. However, the Inertial Measurement Unit (IMU) sensors on most low-cost UAVs are not capable of being directly used for geo-registering the video. The magnetic compasses on UAVs are more vulnerable to the interferences in the working environment than the accelerometers. Thus the camera yaw error is the main sources of the registration error. In this research, to enhance the low accuracy attitude data from the onboard IMU, an extended Kalman Filter (EKF) model is used to merge Real Time Kinematic Global Positioning System (RTK GPS) data with the IMU data. In the merge process, the high accuracy RTK GPS data can be used to promote the accuracy and stability of the 3-axis body attitude data. A method of target localization based on the geo-registration model is proposed to determine the coordinates of the ground targets in the video. The proposed method uses a modified extended Kalman Filter to combine the data from RTK GPS and the IMU to improve the accuracy of the geo-registration and the localization result of the ground targets. The localization results are compared to the reference point coordinates from satellite image. The comparison indicates that the proposed method can provide practical geo-registration and target localization results.
Highlights
Unmanned Aerial Vehicles (UAVs)-based target monitoring plays an integral part in multiple areas such as traffic management [1,2,3], forest-fire control [4,5], border and port patrolling [6], wild animal tracking [7]and emergency management [8]
The proposed method uses a modified extended Kalman Filter to combine the data from Real Time Kinematic Global Positioning System (RTK GPS) and the Inertial Measurement Unit (IMU) to improve the accuracy of the geo-registration and the localization result of the ground targets
An Allan variance analysis is performed on the IMU data from the UAV to determine the error parameters of the IMU, the results are shown in Table 1: Table 1
Summary
UAV-based target monitoring plays an integral part in multiple areas such as traffic management [1,2,3], forest-fire control [4,5], border and port patrolling [6], wild animal tracking [7]and emergency management [8]. UAV-based target monitoring plays an integral part in multiple areas such as traffic management [1,2,3], forest-fire control [4,5], border and port patrolling [6], wild animal tracking [7]. Conventional monitoring methods include fixed station monitoring, satellite monitoring and human-crewed aircraft monitoring [1]. Satellites can monitor vast areas, but their revisit time is too long for time-sensitive monitoring missions due to the orbit limitations. A single satellite needs typically tens of hours to revisit a particular target area, and tracing a moving target is nearly impossible. Human-crewed aircraft are responsive in monitoring tasks, and the onboard sensors can satisfy the spatial and temporal resolution requirements
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