RANSAC

This study investigates robust keypoint detection and geometric image matching in high-resolution UAV imagery of agricultural fields – an essential component for precision farming, crop monitoring, yield prediction, and automated field boundary mapping. While unmanned aerial vehicle (UAV) systems provide high spatial resolution and flexibility, aligning multiple images into coherent mosaics remains a technical challenge, particularly in agricultural settings where repetitive structures, low texture, and illumination variations are prevalent.

Mobile robots control systems achieve greater efficiency through the use of robust environmental analysis algorithms based on data collected from optical sensors such as depth cameras, Light Detection and Ranging sensors (LIDARs). These data sources provide information about control object environment in point cloud. The work of such algorithms, as a rule, is aimed at detecting the objects of interest and searching for the specified objects, as well as relocating its own position on the scene.