Title: Precision UAV Landing in Unstructured Environments

Abstract:
The autonomous landing of a drone is an important part of autonomous flight. One way to have a high certainty of safety in landing is to return the drone to the same location it took-off from. Current implementations of the return-to-home functionality fall short when relying solely on GPS or odometry as inaccuracies in the measurements and drift in the state estimate guides the drone to a position with a large offset from the initial position. This situation can be particularly dangerous if the drone took-off next to something like a body of water. Current work on precision landing relies on localizing to a known landing pattern, which requires the pilot to carry a landing pattern with them. We propose a method using a downward facing fisheye lens camera to accurately land a UAV from where it took off on an unstructured surface, without a landing pattern. Specifically, this approach uses a position estimate relative to the take-off path of the drone to guide the drone back to its original starting point. With the large field-of-view provided by the fisheye lens our algorithm can provide visual feedback starting with a large position error at the beginning of the landing to 1 m above the ground at the end of the landing. This algorithm empirically shows it can land with an accuracy of 40 cm.

Committee:
Sebastian Scherer (Advisor)
Michael Kaess
Kumar Shaurya Shankar