Multi Sensor Fusion For Enhanced 3D Pose Estimation Of Unmanned Aerial Vehicle

This paper aimed to increase the 3D pose of Unmanned Aerial Vehicles (UAVs) through a multi-sensor fusion system based on an Extended Kalman Filter. Sensor data integration from IMU and GPS provided an effective solution to problems like signal loss of GPS, drift of the IMU sensor, and magnetic interference. Simulated sensor data validated the capability of the system to provide efficient navigation, stabilization, and control in dynamic hostile environments. This proposed approach therefore has the potential to mitigate the individual limitations of sensors and allow for reliable real-time UAV operations and advancement of the development of autonomous systems into practical deployment.