Towards the implementation of vision-based UAS sense-and-avoid
Mejias, Luis, Ford, Jason J., & Lai, John S. (2010) Towards the implementation of vision-based UAS sense-and-avoid. In Grant, Ian (Ed.) Proceedings of the 27th International Congress of the Aeronautical Sciences(ICAS 2010 CD-Rom ), International Congress of the Aeronautical Sciences, Acropolis Conference Centre, Nice.
Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of the sensors involved. This paper describes the development of detection algorithms and the evaluation of a real-time flight ready hardware implementation of a vision-based collision detection system suitable for fixed-wing small/medium size UAS. In particular, this paper demonstrates the use of Hidden Markov filter to track and estimate the elevation (β) and bearing (α) of the target, compares several candidate graphic processing hardware choices, and proposes an image based visual servoing approach to achieve collision avoidance
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:||Filtering techniques, Detection algorithms, UAS sense and avoid, Obstacle avoidance, Computer vision|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)|
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100)
|Divisions:||Current > Research Centres > Australian Research Centre for Aerospace Automation|
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2010 [please consult the authors]|
|Deposited On:||11 Oct 2010 10:18|
|Last Modified:||01 Mar 2012 00:21|
Repository Staff Only: item control page