Vision-based map building and trajectory planning to enable autonomous flight through urban environments

The desire to use Unmanned Air Vehicles (UAVs) in a variety of complex missions has motivated the need to increase the autonomous capabilities of these vehicles. This research presents autonomous vision-based mapping and trajectory planning strategies for a UAV navigating in an unknown urban environment.;It is assumed that the vehicle's inertial position is unknown because GPS in unavailable due to environmental occlusions or jamming by hostile military assets. Therefore, the environment map is constructed from noisy sensor measurements taken at uncertain vehicle locations. Under these restrictions, map construction becomes a state estimation task known as the Simultaneous Localization and Mapping (SLAM) problem. Solutions to the SLAM problem endeavor to estimate the state of a vehicle relative to concurrently estimated environmental landmark locations. The presented work focuses specifically on SLAM for aircraft, denoted as airborne SLAM, where the vehicle is capable of six degree of freedom motion characterized by highly nonlinear equations of motion. The airborne SLAM problem is solved with a variety of filters based on the Rao-Blackwellized particle filter. Additionally, the environment is represented as a set of geometric primitives that are fit to the three-dimensional points reconstructed from gathered onboard imagery.;The second half of this research builds on the mapping solution by addressing the problem of trajectory planning for optimal map construction. Optimality is defined in terms of maximizing environment coverage in minimum time. The planning process is decomposed into two phases of global navigation and local navigation. The global navigation strategy plans a coarse, collision-free path through the environment to a goal location that will take the vehicle to previously unexplored or incompletely viewed territory. The local navigation strategy plans detailed, collision-free paths within the currently sensed environment that maximize local coverage. The local path is converted to a trajectory by incorporating vehicle dynamics with an optimal control scheme which minimizes deviation from the path and final time.;Simulation results are presented for the mapping and trajectory planning solutions. The SLAM solutions are investigated in terms of estimation performance, filter consistency, and computational efficiency. The trajectory planning method is shown to produce computationally efficient solutions that maximize environment coverage.