Indoor Pedestrian-Following System by a Drone with Edge Computing and Neural Networks: Part 1 - System Design

As the drone market continues to expand, the need for accurately determining a drone's position and orientation using a camera in GPS-denied environments becomes increasingly critical. This paper aims to achieve precise position and attitude data by incorporating SLAM to provide visual measurements for EKF, thereby ensuring the stability of drone operations. An experiment was conducted to execute commands from the ground control PC using the map created as a result of SLAM. The primary tools used for this purpose included the Pixhawk Orange, Jetson Nano, and the ZED-Mini camera. The research showcases the effectiveness of these tools and methods in enhancing indoor drone functionality.