  Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone)
industry has expanded possibilities for fully autonomous UAV applications. A
particular application which has in part motivated this research is the use of
UAV in wide area search and surveillance operations in unstructured outdoor
environments. The critical issue with such environments is the lack of
structured features that could aid in autonomous flight, such as road lines or
paths. In this paper, we propose an End-to-End Multi-Task Regression-based
Learning approach capable of defining flight commands for navigation and
exploration under the forest canopy, regardless of the presence of trails or
additional sensors (i.e. GPS). Training and testing are performed using a
software in the loop pipeline which allows for a detailed evaluation against
state-of-the-art pose estimation techniques. Our extensive experiments
demonstrate that our approach excels in performing dense exploration within the
required search perimeter, is capable of covering wider search regions,
generalises to previously unseen and unexplored environments and outperforms
contemporary state-of-the-art techniques.
