Keyboard Based Robust Remote Operation of UAV in GPS-Denied and Obstacle Rich Environment

Unmanned Aerial Vehicles (UAVs) have seen a rise in applications to various fields. With plenty of algorithms to support automation in UAV flights, Global Positioning System (GPS) is still the major source of position estimation. This has limited the application of UAVs to areas where GPS signal is available and strong. Thus, some other method of position estimation for the UAV is required to expand the UAV application to GPS-denied areas. Moreover, when an operator is piloting a UAV from a remote location, the operator is solely relying on the camera feed coming from the UAV to move the UAV. This camera feed gives a limited field of view of the environment, and the human operator may accidentally run the UAV into an obstacle. In this research, a method of using Hector SLAM for performing position estimation of the UAV in a GPS-denied indoor environment is presented. The Hector SLAM uses a 2D LiDAR mounted on top of the quadcopter to scan the unknown environment. Furthermore, to empower the UAV to autonomously avoid obstacles, an algorithm using Artificial Potential Field method is developed in this thesis which maneuvers the UAV away from obstacles while being piloted by a human operator. The system is developed using Robot Operating System (ROS) and PX4 autopilot. Two different ways, setpoints and attitude commands, of operating the UAV using a keyboard are implemented and compared. The algorithm has been tested in Gazebo Classic simulator and its performance is evaluated.