Real-Time Sensor Fusion for Enhanced Situational Awareness in UGVs

Abstract

Situational awareness is vital for the autonomous operation of Unmanned Ground Vehicles (UGVs) in complex and dynamic environments. This paper presents a real-time sensor fusion framework designed to enhance situational awareness in UGVs, enabling them to perceive and understand their surroundings with greater accuracy and reliability. Our approach integrates data from multiple sensors, including LIDAR, cameras, GPS, and inertial measurement units (IMUs), using advanced fusion algorithms to provide a comprehensive view of the environment. By combining sensor inputs in real time, the system compensates for individual sensor limitations, resulting in improved obstacle detection, localization, and path planning capabilities. Field experiments demonstrate that the sensor fusion system significantly improves UGVs' ability to navigate safely and autonomously in diverse and unpredictable environments. This paper highlights the importance of multi-sensor integration for real-time decision-making and explores potential applications across industries such as defense, agriculture, and disaster relief.