Unmanned air vehicles have been increasing in their autonomous capabilities. This research furthers these capabilities by focusing on the automation of landing site determination for rotorcraft in urban environments. Automated landing saves energy and allows the aircraft to choose areas that are safe for people and the aircraft. Two methods are used to gather information about the terrain of potential landing sites. One method is 3D reconstruction from multiple camera images. The other method uses a range sensor to reconstruct the terrain. Both of these methods create an inertial terrain map of the environment in the form of a point cloud that can be investigated for possible landing sites. Two strategies were developed to search the terrain map for possible landing sites: grid-based RANSAC and Recursive-RANSAC (R-RANSAC). Both strategies search for flat stable areas for landing. Grid-based RANSAC separates the terrain map into discrete portions for plane fitting analysis. These fitted planes are used to determine whether portions of the terrain map are suitable for landing. Two additional variations of grid-based RANSAC were explored that resulted in improvements to the approach. This strategy can quickly find landing sites from large terrain maps. The other strategy, R-RANSAC, is a recursive approach that analyzes each point in the terrain map for plane fitting. New planes are created as needed to fit points in the terrain map. Planes that fit a large number of points are analyzed for possible landing locations. This strategy is more complex to implement, but results in a simpler model of the environment: a small set of 3D planes. The results are displayed with the possible landing locations. Both landing-site strategies were implemented onboard a hexrotor aircraft and successfully demonstrated in flight.
| Identifer | oai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-6530 |
| Date | 01 July 2014 |
| Creators | Mackay, Justin Keith |
| Publisher | BYU ScholarsArchive |
| Source Sets | Brigham Young University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | http://lib.byu.edu/about/copyright/ |
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