Comparative Review of Navigation Systems for Indoor Autonomous Unmanned Aerial Vehicles

Introduction. In recent years, unmanned aerial vehicles (UAVs) have been a rapidly advancing field. In all areas of UAV application, positioning accuracy is of particular importance. For outdoor environments, satellite navigation systems (such as GPS) are always the method of choice. However, for indoor environments, GPS signal weakening becomes a serious obstacle for determining the UAV location. A number of studies have been carried out to develop various indoor positioning technologies that meet the criteria of compactness and light weight, thus suitable for small aircrafts, including optical flows, inertial measurement systems, ultrasound, etc. However, there is a lack of comparative studies reviewing indoor positioning technologies for autonomous UAVs. The existing reviews fail to provide a comprehensive assessment of such technologies and their operational principles according to the main criteria. In this connection, this paper aims to review modern indoor positioning technologies and their operational principles, conducting evaluation according to such criteria as accuracy, operating range, cost. The assessment of promising machine vision-based technologies is carried out.

Aim. To classify modern indoor navigation technologies for UAVs; to assess the technologies under consideration according to various criteria.

Materials and methods. The current technologies for UAV indoor positioning were classified by the signal type used for connection and their capability to process information without external signals. The technologies were assessed according to the following criteria: accuracy, operating range, cost, as well as their advantages and disadvantages.

Results. А classification and evaluation table of UAV indoor positioning technologies is proposed; a review of the current developments in the field is given.

Conclusion. A review of UAV indoor positioning technologies has been carried out. In addition, the prospects of machine vision-based technologies are outlined.

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