UAV based accurate displacement monitoring through automatic filtering out its camera's translations and rotations

In this paper, three translations and three rotations of the unmanned aerial vehicle (UAV) were derived through the suggested Normalized Cross Correlation (NCC) based template matching computer vision method and their effect on the monitored structural displacement is analyzed. The NCC-based template matching is used to obtain the vibration of UAVs by tracing far-field background points at the recorded images. The images captured at any general orientations are rotated back to their original positions which could detect errors in the monitored displacement induced by the rotation angles. A fast NCC-based template matching method was also proposed in this paper, which could accelerate the original NCC-based template matching method significantly and reaches the same level accuracy as that of the original NCC-based template matching method. In order to verify the concept, a series of experiments were performed on a Multipurpose Testing System (MTS) machine with amplitudes of 10 mm, 5 mm, and 2.5 mm and frequencies of 2 Hz, 1 Hz and 0.5 Hz, respectively, at the same time a UAV and a fixed camera were used to record the motions of the MTS piston. The derived displacements through the UAV and the fixed camera are compared with the true motions of the MTS machine. Excellent precision and consistence were obtained for the UAV monitored displacement, the MTS piston motion, and the fixed camera derived displacement. Comparing to the case of the fixed camera, scales and rotation angles of the UAV are critical and largely affect the accuracy of monitored displacements. A field experiment was conducted to test the proposed method in a windy environment. Excellent agreement was found between the monitored displacement obtained by the UAV and that derived from the fixed camera.