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Vision‐Based Aircraft Localization During the End Stage of Landing Approach via Runway Mark Determination
by
Duan, Botao
, Han, Chenglong
, Xu, Hansong
, Wu, Jun
, He, Zhuoyao
in
Accuracy
/ Aircraft
/ Aircraft landing
/ Airports
/ Algorithms
/ Aviation
/ Cameras
/ Corners
/ Edgelines
/ Investigations
/ Localization
/ Neural networks
/ Parameter estimation
/ Pose estimation
/ Quadrilaterals
/ Runways
2026
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Vision‐Based Aircraft Localization During the End Stage of Landing Approach via Runway Mark Determination
by
Duan, Botao
, Han, Chenglong
, Xu, Hansong
, Wu, Jun
, He, Zhuoyao
in
Accuracy
/ Aircraft
/ Aircraft landing
/ Airports
/ Algorithms
/ Aviation
/ Cameras
/ Corners
/ Edgelines
/ Investigations
/ Localization
/ Neural networks
/ Parameter estimation
/ Pose estimation
/ Quadrilaterals
/ Runways
2026
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Do you wish to request the book?
Vision‐Based Aircraft Localization During the End Stage of Landing Approach via Runway Mark Determination
by
Duan, Botao
, Han, Chenglong
, Xu, Hansong
, Wu, Jun
, He, Zhuoyao
in
Accuracy
/ Aircraft
/ Aircraft landing
/ Airports
/ Algorithms
/ Aviation
/ Cameras
/ Corners
/ Edgelines
/ Investigations
/ Localization
/ Neural networks
/ Parameter estimation
/ Pose estimation
/ Quadrilaterals
/ Runways
2026
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Vision‐Based Aircraft Localization During the End Stage of Landing Approach via Runway Mark Determination
Journal Article
Vision‐Based Aircraft Localization During the End Stage of Landing Approach via Runway Mark Determination
2026
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Overview
Autonomous landing of aircraft during the approaching stage is a crucial part of the autonomous driving of aircraft. This paper mainly investigates the localization problem of aircraft during the end stage of the approaching process via a visual technique. To robustly identify the mark corners, a dedicated algorithm was proposed for a convex quadrilateral. The results proved its effectiveness, and the way for the marks′ edge line optimization was also presented. Moreover, different ways for updating runway parameters as well as selecting key points for pose estimation were also investigated in detail. It proved that continuously updating runway parameters related to mark corners distant from the camera with those close to the camera helps greatly improve the quality of runway parameter estimation. Besides, updating runway parameters forward favors the accuracy of runway parameters estimated, as runway parameters related to mark corners close to the camera hold better accuracy than those far from the camera. It also proved that taking mark corners as the key points is not a good way for pose estimation, as it involves errors at both the x and y coordinates. However, using the cross points between the side lines′ edges and the runway marks′ horizontal edge lines as the key points was much better, as it only involves an inaccuracy in the y coordinate. According to this investigation, cross points related to horizontal lines close to the camera are essential for ensuring pose estimation accuracy, while taking as many horizontal lines as possible for generating key points with side lines helps improve pose estimation accuracy.
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