Self-localization of drones in non-GPS environments

Abstract

The social development of drones is progressing rapidly, and they are beginning to be used in many industrial fields. Autonomous position estimation in non-GPS environments aims to expand the usability of unmanned aerial vehicles (UAVs) where GPS signals are unavailable, such as underground, indoors, or areas obstructed from satellite signals. This is particularly relevant for search and rescue operations during disasters and complex industrial applications. This research proposes a method that integrates multiple sensors, including RGBD cameras, IMUs, LiDAR, and GNSS, enabling the UAV to autonomously select and utilize the optimal sensor data for precise self-localization. We develop algorithms for sensor data fusion, environmental perception, and sensor selection and switching to ensure advanced autonomy in unmanned aerial systems. This framework is designed to enhance the operational capabilities of UAVs in GPS-denied environments, broadening the scope of their application.