UAV Stabilization System Based on Machine Vision and Inertial Sensors

Abstract

Unmanned aerial vehicles (UAVs) are widely used in areas such as remote monitoring in agricultural industry, infrastructure inspection, logistics, and exploration of hard-to-reach areas. However, many existing approaches to stabilization and navigation rely on global navigation satellite systems, whose effectiveness is reduced in conditions with limited or no satellite signal. The objective of this study is to develop a UAV stabilization system based on machine vision and inertial sensors for operation in GNSS-denied environments. The proposed method uses visual-inertial odometry implemented using the OpenVINS framework, fusing camera data and inertial measurements within an extended Kalman filter. The system is implemented as a distributed software architecture with a client-server model, data transfer via TCP, and integration with the robot operating system for real-time data processing and state estimation. The developed system ensures reliable collection, synchronization, and processing of visual and inertial data, and enables UAV position and orientation estimation under correct acquisition and data synchronization conditions. A stabilization mechanism based on maintaining a predetermined hover point and generating corrective control inputs was implemented in a simulation environment. The proposed system lays the foundation for further research to improve estimation accuracy and refine control strategies for autonomous UAV operation