Mapping, tree detection, localization, and autonomous flight of unmanned aerial vehicles in forest applications

Two forest management tasks which are important for healthy forest growth are the cleaning of seedling stands and the prevention of moose damage. These management tasks are still largely done manually. They are time-consuming, labour-intensive, and likely to face a labour shortage in the near future. This thesis has two aims: (1) to develop approaches that use images collected by a UAV platform to support the semiautonomous cleaning of a seedling stand and (2) to develop methods that enable UAVs to autonomously and selectively spray a bio-based repellent to prevent moose damage. In the first two articles of the thesis, deep learning methods are used to detect seedling trees in the images collected by a low-cost UAV platform, thereby creating a map of the seedling trees. A graph-based registration approach is developed to localize the forest cleaning machine within the map. A human-machine interface is also developed to incorporate the registration algorithm and to show in real-time the seedling trees and the cleaning tool to the machine operator. The approaches developed in these two articles demonstrate the feasibility of the UAV-assisted semiautonomous cleaning of seedling stands. The last three articles of this thesis develop approaches for state estimation, loop closure detection, seedling tree detection from LiDAR data, and path planning. A pose-graph state estimation approach is developed, achieving accurate position, attitude, and consistent velocity estimates. A novel loop closure method using surface variation features is developed to correct the drift in pose estimation. A region-growing algorithm is developed to segment the individual seedling trees from the LiDAR point cloud. Finally, a path planning utilizing an octree data structure, and the informed RRT* approach is used to plan collision-free paths enabling the UAV to fly from the top of a tree to another. These approaches solely rely on IMU and LiDAR sensors; they are fully implemented on a single board computer onboard the UAV platform, and they achieve real-time performance. Several real-world experiments have been conducted to test the methods developed in this thesis, including the autonomous flight in a seedling forest stand.