Algorithm for navigation on the terrain of unmanned aerial vehicles with machine vision

One of the problems solved by developers of autonomously controlled unmanned aerial vehicles is the task of determining by the drone its exact position over the terrain without the help of global satellite navigation systems. The existing mass-dimensional and energy limitations for small-sized drones lead to the necessity of using relatively simple algorithms in drone computing devices. The paper considers methods of navigation of unmanned aerial vehicles using computer vision implemented by on-board optical and computing devices. Machine vision implemented by on-board computing devices provides autonomy of small-sized aircraft in the absence or unstable communication channel with the control center and/or satellite navigation system. The proposed algorithm solves the problem of identifying an area of terrain observed from a drone with a terrain image stored in the memory of the drone control system. The drone location is determined by the minimum (maximum) value of the discrepancy between the observed current image and the image of the terrain area stored in the drone memory device. The solution of the identification problem is based on the concept of immunocomputing using singular value decomposition of the feature matrix of the identified objects. This approach allows providing high quality indicators of identification due to decomposition of the feature matrix into three simple transformations for transition to a new feature space which is not identifiable, but whose components are statistically significant. The quality indicators of the developed algorithm were evaluated in comparison with the known method of image identification by calculating the correlation function between two arrays of features. A series of tests were carried out in which the probability of correct location determination and the speed of the algorithms were evaluated for the same initial data. It is shown that when pre-preparing a “reference” image stored in the drone memory device, the speed of the developed method exceeds the speed of the method based on the calculation of the correlation function of the compared images by an order of magnitude. The mean absolute error of correct positioning using the proposed method ranges from 0.109 to 0.153. The proposed algorithm can be used by developers of navigation systems for small-sized unmanned aerial vehicles due to its low resource requirements while maintaining a level of accuracy sufficient in the context of solving problems of orientation on the terrain. Devices realizing the proposed orientation algorithm have better energy and mass-size characteristics.

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