Today, the development of indoor positioning systems is a promising and relevant, but difficult task. This requires the creation of maps based on floor plans, the selection of effective location algorithms, and the creation of an appropriate building infrastructure for reliable location determination. Existing positioning systems that use radio channels for indoor navigation, infrared and ultrasonic tagging are considered. It is established that the use of a radio channel indoors is problematic due to interference and signal reflections that affect accuracy. The analysis showed that the most reliable channel for use indoors is a channel in the visible range of the electromagnetic spectrum. Such systems work using video cameras and algorithms. Systems known from open sources that use video cameras and machine vision systems are considered. The main problem of existing indoor positioning technologies based on applied video surveillance systems is their connection with the working background, such as the interior, or the requirement of a uniform background for the use of colored beacons. Based on the analysis, the use of colored beacons as reference points and a video measurement system for calculating distances to these points for indoor navigation are proposed. The use of colored beacons is proposed based on the hypothesis that they can work in conditions of a heterogeneous background, unlike existing systems. To confirm these assumptions, appropriate studies were conducted. An algorithm for recognizing colored lights on an input video image obtained from a digital image processing video camera has been developed. The algorithm is based on constructing a "color mask" using a smooth continuous logistic sigmoid curve and a Gaussian function to extract the color characteristic of the light signal. An algorithm for determining the coordinates of the marker recognition point based on recognized colored beacons in frames of the input video image has been developed. To determine the coordinates of a mobile robotic system, measurements are taken on the video image to determine the relative coordinates of the beacons in the camera coordinate system. A relative map is constructed, which is converted into absolute camera coordinates in the world coordinate system using a three-dimensional transformation. This is made possible by the predefined absolute coordinates of the color beacons. Filtering parameters for the color beacon recognition algorithm are defined, which allows the algorithm to maintain performance under conditions of heterogeneous backgrounds. The following parameters are used: the variance of the Gaussian curve for hue filtering, the curve and logistic threshold shift of the sigmoid curve for saturation; the logistic curvature of the sigmoid curve and the brightness threshold shift. These parameters allow classifying the pixels of the input image by color, thereby identifying only those areas that correspond to the color values of the color beacon areas under variable lighting conditions.
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