Synthesis of localization and odometry for positioning mobile robots in enclosed space

The positioning of robotic systems is a key aspect of the development and operation of autonomous robots. This topic covers many points, such as navigation, perception of the environment, data processing, and motion planning. The determination of the robot’s position in space is an essential step in the creation of any mobile robotic system. Effective positioning allows robots to identify their exact location in space and relative to surrounding
objects, which is crucial for performing various procedures. Modern positioning approaches include the use of many kinds of sensors and technologies, such as inertial measurement units, cameras, and laser rangefinders. The integration of diverse data sources allows not only to increase accuracy but also to ensure the reliability of positioning systems in conditions where one type of sensor may malfunction. This article analyzes common approaches to local object positioning, omitting global positioning technology. The process of combining and integrating the specified methods is assessed to enhance the accuracy and dependability of localization results within the surrounding area. Specifically, a solution is provided for the optimal non-linear filtration problem, which employs an extended Kalman filtering approach. A comparative analysis of the advantages and disadvantages of
the most common approaches in defining the robot’s location is performed, and a recommended mechanism for achieving maximum accuracy in carrying out the positioning task is determined

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