Autonomous systems: Indoor drone navigation

In this research work, we present an advanced autonomous indoor navigation system for drones, leveraging the Nav2 navigation system framework and SLAM Toolbox in ROS (Robot Operating System) within the Gazebo simulation environment. Unlike previous implementations which primarily focused on terrestrial robots and land vehicles, our system introduces significant innovations in the context of UAVs (Unmanned Aerial Vehicles). The proposed solution addresses the critical challenge of precise localization and mapping in complex indoor settings, where GPS signals are unavailable. This research contributes to the field by showcasing a simulated quadcopter's capability to autonomously navigate and traverse obstacles in an indoor environment. Distinctly, our approach extends the functionalities of the Nav2 framework, previously validated in ground vehicles, to UAVs, demonstrating its adaptability and potential for broader applications. This innovation represents a substantial leap over existing systems, offering a novel use case for indoor UAV navigation, which is critical for tasks such as emergency rescue operations, inspection, and surveillance in GPS-denied environments.