Abstarct. An important task for autonomous robots is to navigate safely in unfamiliar environments, potentially using computer vision to detect and recognize obstacles. Vision-based control systems have been developed for several years. Some rely on artificial landmarks, while more advanced systems make use of natural landmarks.The latter approach is preferable when a robot must operate in real, unstructured environments.
In the field of autonomous robot navigation, which includes map building, path planning, and self-localization, this work develops the concept of a simple autonomous agent relying exclusively on visual information. The integrated navigation system reproduces certain functions of natural systems, as it requires minimal prior knowledge, limited onboard computation, and lacks an omnidirectional field of view. Since the goal is to move the robot across the floor while avoiding obstacles and people, the camera is mounted on top of the robot in a fixed forward-facing position. The article focuses on one of the fundamental tasks of image processing—detecting the boundaries of objects in the observed scene. The aim of the research is to study contour detection algorithms based on preliminary image filtering and to compare the proposed approaches with well-known edge detectors such as Sobel, Canny, and Laplacian of Gaussian. Preliminary filtering is used to suppress image noise and enhance edges. The scientific novelty of the work lies in the development and experimental evaluation of a contour detection algorithm that incorporates a pre-filtering stage based on contrast enhancement, the Kalman filter, and Monte Carlo methods. This increases the robustness of video processing for mobile robots operating in noisy environments. The Sobel, Canny, and LoG algorithms were comprehensively analyzed and compared using a set of metrics, including the number of lost pixels, mean squared error, normalized MSE, and the structural similarity index. This approach provided a deeper understanding of their effectiveness under different noise conditions.

Keywords: computer vision, image processing algorithms, Kalman filter, Monte Carlo methods, image segmentation, Laplacian of Gaussian, Sobel and Canny algorithms, real-time video analysis.