Robust Sensor Fusion for Autonomous UAV Navigation in GPS denied Forest Environment

Forestry and precision biodiversity data collection using UAVs can be cost-effective and time efficient solution. However, navigating in the forest canopy autonomously can be quite challenging because of its GPS denied environment, cluttered, dynamic, and large scale. Most of the commercial UAVs used in forest applications apply GPS-based navigation which is not suitable for navigating under the canopy. In this paper, an autonomous UAV flight mission in a cluttered forest-like canopy environment is presented. A robust multi-sensor fusion-based robust navigation method which has failure detection features is proposed to enable safe and reliable autonomous navigation. The autonomy architecture utilizes the navigation, planning, and control capabilities of the UAV in a simultaneous manner. To achieve autonomous missions in forest environment, the proposed system has been tested rigorously in a simulated environment and the result shows the capability of autonomous flights in such a challenging environment. The performance of the autonomous flight was evaluated based on mean velocity and path length with respect to the increasing number of trees in the forest.