This thesis describes a new approach for a vision-based positioning system for Un-
manned Aerial Vehicles using a recognition method based on known, robust geo-
graphic landmarks. Landmarks are used to calculate a position estimate in a global
coordinate frame without requiring external signals, such as GPS. Absolute systems
are of interest as they provide a redundant positioning system, allow UAVs to oper-
ate when GPS-denied and can enable high-precision landings for spacecraft.
The core challenge with vision-based absolute positioning is recognition of land-
marks. Most abundant landmarks, such as buildings, are visually similar and dif-
cult to distinguish. Previous research in the area tends to focus on matching raw
aerial image data to a set of reference images. While these methods can achieve
acceptable results in speci c conditions, they struggle with variations in lighting,
seasonal changes and changing environments. This thesis presents a new multi-
stage method that aims to solve this using a high-level matching framework where
landmarks identi ed in an aerial image are matched to a reference database.
This has led to the development of a geometric feature descriptor that encodes the
topography of landmarks. The proposed system therefore matches the arrangement
of features rather than the appearance, which lets it distinguish individual landmarks
in large sets (20,000+ features). Since the arrangement of landmarks often is semi-
structured and ambiguous, in particular when considering man-made landmarks,
a matching stage has been developed that uses a number of strategies to enable
matching of individual landmarks to a full database.
The results have been evaluated for two conceptual vehicles with acceptable results,
highlighting the strengths of the proposed system as well as areas for improve-
ment.
| Identifer | oai:union.ndltd.org:CRANFIELD1/oai:dspace.lib.cranfield.ac.uk:1826/9249 |
| Date | 08 1900 |
| Creators | Mannberg, Mikael |
| Contributors | Savvaris, Al |
| Publisher | Cranfield University |
| Source Sets | CRANFIELD1 |
| Language | English |
| Detected Language | English |
| Type | Thesis or dissertation, Doctoral, PhD |
| Rights | © Cranfield University, 2014. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. |
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