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This paper presents an approach to improving mobile robot localization by filtering dynamic objects using camera image segmentation. The proposed algorithm integrates a Particle Filter with state-of-the-art computer vision techniques, specifically employing the YOLO model for segmentation, which effectively differentiates static elements of the environment from moving objects. This approach reduces the impact of noisy data and enhances localization accuracy in dynamic conditions, which is crucial for the reliable autonomous operation of mobile robots.

One of the key problems in the implementation of closed-loop control systems is to measure all states of a dynamic system, especially, when there are severe environmental conditions. Consequently, the use of certain types of sensors is impossible for technical or economic reasons. Also, in electromechanical systems, there are a lot of values that cannot be directly measured by physical sensors. Thus, mathematical algorithms named as observers and estimators are in use to calculate the states of the dynamic system utilizing math model and available set of sensors.