Analysis of Algorithms for Georeferenced Positioning of an Unmanned Aerial Vehicle Using Visual-Inertial Odometer Data

Known algorithms for detecting and matching singular image points are presented when solving the problem of autonomous positioning of an unmanned aerial vehicle (UAV) equipped with a video camera in the absence of a signal from the global satellite navigation system. The idea of autonomous UAV positioning was to sequentially compare adjacent pairs of video stream frames to track the movements of the video camera carrier in the time interval between the mentioned frames. The purpose of the study is to identify those that best allow tracking the coordinates of the carrier based on the results of a comparative analysis of algorithms. The real flight data used to construct the control trajectory was obtained using a mock-up model of an aircraft-type aircraft equipped with an on-board inertial navigation system with a set of microelectromechanical sensors and a visual odometer with a nadir-oriented video camera. The comparison of real flight data and the results of the algorithms was carried out according to several quality indicators: the minimum standard deviation of the positioning error, the maximum deviation from the control trajectory, and the average time required to process a pair of adjacent frames. The results of comparing the algorithms are summarized in a table, and graphs of the control trajectory and estimates obtained using these algorithms are also provided for visual comparison. According to the set of positioning quality indicators, the neural network algorithms d2net, darkfeat and XFeat(dense) are recommended for implementation.

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