Overview of Visual Position Recognition

Abstract

One of the promising areas in the development of artificial intelligence is the creation of computer vision technology – a system that enables computerized systems to acquire, analyze, and interpret information from photos, videos, or digital images. This opens up extensive opportunities for process automation across various fields, including robotics, autonomous transportation, industry, and medicine. One of the key challenges in computer vision research is the problem of visual position recognition – assessing a robot’s coordinates and orientation based on video or photo data obtained from its cameras. In robotic systems, precise position recognition is critical for navigation, adaptation to environmental changes, and interaction with objects. The article attempts to formulate the problem in terms of approximating the probability density function of the robot’s states within the space of input data. In addition to theoretical aspects, the study examines a set of algorithms currently in use – both classical approaches and neural network-based models – their universality, and their integration potential with other computer vision technologies. The interpretation of these algorithms is presented from the perspective of dimensionality reduction in the input data space during localization. Furthermore, a list of relevant datasets for training and testing visual position recognition models is provided, along with key metrics for evaluating their performance. Thus, the study not only summarizes modern approaches to solving this problem but also outlines directions for further technological advancements that can ensure more efficient and accurate robot localization in space.