System Development and Integration of Unmanned Aerial Vehicle Navigation Techniques for Indoor Environments

In this paper we present the exploration of indoor positioning technologies for UAV s, and navigation techniques for path planning and obstacle avoidance. For the indoor positioning techniques, we employed Visual-Inertial Odometry (VIO), Ultra-Wideband (UWB), AprilTag fiducial markers, and Simultaneous Localization and Mapping (SLAM). These algorithms encompass global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. In UAV navigation, we combined SLAM-based RTAB-Map indoor mapping and navigation path planning of the ROS for indoor environments. This system enables precise drone positioning indoors and utilizes global and local path planners to generate flight paths that avoid dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. The feasibility of our UAV path planning and obstacle avoidance navigation algorithms is tested in real and virtual environments. Code is available at https://github.com/kellen080INavigation and https://github.com/kellen080IIndoor_Positioning