Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments
This addresses the problem of enabling fully autonomous UAVs for search and surveillance in unstructured environments like forests, which is incremental as it builds on existing pose estimation methods.
The paper tackles autonomous UAV flight control in unstructured outdoor environments lacking features like roads, proposing a multi-task regression-based learning approach that defines flight commands for navigation and exploration under forest canopies without GPS. The results show it excels in dense exploration, covers wider search regions, generalizes to unseen environments, and outperforms state-of-the-art techniques.
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.