Enhancing UAV Autonomy and Path Optimization Through SBAS and Visual-Inertial Odometry

Unmanned Aerial Vehicles (UAVs) have become indispensable in fields such as disaster response, precision agriculture, environmental monitoring, and surveillance. Their ability to navigate complex and dynamic environments makes them essential for autonomous operations. However, ensuring accurate and reliable state estimation remains a significant challenge, particularly in GPS-denied environments, where traditional navigation systems suffer from drift, localization errors, and trajectory inconsistencies. Addressing these limitations is crucial for improving UAV autonomy and operational efficiency. This study aims to enhance UAV autonomy and trajectory optimization by integrating a Satellite-Based Augmentation System (SBAS) with monocular visual-inertial odometry (VIO) within a factor graph optimization framework. The proposed methodology fuses visual and inertial sensor data, incorporating state constraints and prior knowledge to improve localization accuracy, reduce drift, and manage uncertainty. Experimental evaluations were conducted under different path optimization conditions to assess system performance. Results show that Path 1 achieved the highest optimization score of 0.481, Path 2 showed moderate optimization at 0.130, while Paths 3, 4, and 5 exhibited minimal improvements, with scores of −6.176, −0.041, and −0.113, respectively. These findings confirm the effectiveness of the proposed approach in optimizing UAV trajectories and enhancing real-time navigation accuracy. The study concludes that integrating SBAS with VIO significantly enhances UAV state estimation, offering a promising solution for autonomous aerial operations in both indoor and outdoor environments. This approach provides a robust and scalable framework for improving UAV navigation in critical applications, ensuring greater reliability under GPS-denied conditions.