A Vision Based Forced Landing Site Selection System for an Autonomous UAV
Fitzgerald, Daniel L., Walker, Rodney A., & Campbell, Duncan A. (2005) A Vision Based Forced Landing Site Selection System for an Autonomous UAV. In ISSNIP, December 2005, Melbourne, Australia. (Unpublished)
This paper presents a system overview of the UAV forced
landing site selection system and the results to date. The
forced landing problem is a new field of research for UAVs
and this paper will show the machine vision approach taken
to address this problem. The results are based on aerial
imagery collected from a series of flight trials in a Cessna
The aim of this research is to locate candidate landing sites
for UAV forced landings, from aerial imagery. Output image
frames highlight the algorithm’s selected safe landing
locations. The algorithms for the problem use image
processing techniques and neural networks for the
The system is capable of locating areas that are large enough
to land in and that are free of obstacles 92.3% ± 2% (95%
confidence) of the time. These areas identified are then
further classified as to their surface type to a classification
accuracy of 90% ± 3% (98% confidence).
It should be noted that although the system is being designed
primarily for the forced landing problem for UAVs, the
research can also be applied to forced landings or glider
applications for piloted aircraft.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Uninhabited airborne vehicles (UAV), UAV forced landing, UAV safety, computationally intelligent framework, machine vision, radial basis probabilistic neural networks, classification|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100) > Aerospace Engineering not elsewhere classified (090199)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
|Copyright Owner:||Copyright 2005 (please consult author)|
|Deposited On:||04 Aug 2006|
|Last Modified:||29 Feb 2012 23:16|
Repository Staff Only: item control page