A framework for UAV navigation and exploration in GPS-denied environments

Unmanned Aerial Vehicles (UAV) are increasingly used in a wide range of applications such as civil infrastructure inspection, agriculture, ecology and remote sensing. UAV autonomous operation relies on the use of GPS in order to localise and plan a mission, however there are places in which GPS positioning is limited or not available, such as in urban and natural canyons or below the canopy. This paper presents the development of a framework that enables a drone to navigate in an unknown, unstructured and GPS-denied environment. The aim of this research is to combine the use of localisation algorithms such as Simultaneous Localisation and Mapping (SLAM) with Partially Observable Markov Decision Processes (POMDP) algorithms into a framework in which the navigation and exploration tasks are modelled as sequential decision problems under uncertainty. The framework tested in simulation allows the UAV to navigate safely, avoiding collisions whilst guiding exploration in order to create an occupancy map of the UAV's surroundings. The proposed system guides the UAV through a series of actions in order to maximise the information gain about the unknown environment. The implementation of the proposed framework will enable the use of UAV for autonomous navigation and exploration in challenging environments where GPS positioning is not available or limited.