Improving monocular SLAM with altimeter hints for fixed-wing aircraft navigation and emergency landing
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2015
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Resumo |
This paper presents a visual SLAM method for temporary satellite dropout navigation, here applied on fixed- wing aircraft. It is designed for flight altitudes beyond typical stereo ranges, but within the range of distance measurement sensors. The proposed visual SLAM method consists of a common localization step with monocular camera resectioning, and a mapping step which incorporates radar altimeter data for absolute scale estimation. With that, there will be no scale drift of the map and the estimated flight path. The method does not require simplifications like known landmarks and it is thus suitable for unknown and nearly arbitrary terrain. The method is tested with sensor datasets from a manned Cessna 172 aircraft. With 5% absolute scale error from radar measurements causing approximately 2-6% accumulation error over the flown distance, stable positioning is achieved over several minutes of flight time. The main limitations are flight altitudes above the radar range of 750 m where the monocular method will suffer from scale drift, and, depending on the flight speed, flights below 50 m where image processing gets difficult with a downwards-looking camera due to the high optical flow rates and the low image overlap. |
Formato |
application/pdf |
Identificador | |
Publicador |
IEEE |
Relação |
http://eprints.qut.edu.au/86063/1/andert_mono_alti_nav_icuas15_draft_0122.pdf DOI:10.1109/ICUAS.2015.7152390 Andert, Franz & Mejias, Luis (2015) Improving monocular SLAM with altimeter hints for fixed-wing aircraft navigation and emergency landing. In 2015 International Conference on Unmanned Aircraft Systems (ICUAS 2015), IEEE, Denver, CO, pp. 1008-1016. http://purl.org/au-research/grants/ARC/DE120100802 |
Direitos |
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Fonte |
Australian Research Centre for Aerospace Automation; School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #Forced landing #Visual odometry #Monocular SLAM |
Tipo |
Conference Paper |