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Efficient 7D Aerial Pose Estimation
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6096-3648
(Vricon Systems, SAAB)
2013 (English)In: 2013 IEEE Workshop on Robot Vision (WORV), IEEE , 2013, 88-95 p.Conference paper, Published paper (Refereed)
Abstract [en]

A method for online global pose estimation of aerial images by alignment with a georeferenced 3D model is presented.Motion stereo is used to reconstruct a dense local height patch from an image pair. The global pose is inferred from the 3D transform between the local height patch and the model.For efficiency, the sought 3D similarity transform is found by least-squares minimizations of three 2D subproblems.The method does not require any landmarks or reference points in the 3D model, but an approximate initialization of the global pose, in our case provided by onboard navigation sensors, is assumed.Real aerial images from helicopter and aircraft flights are used to evaluate the method. The results show that the accuracy of the position and orientation estimates is significantly improved compared to the initialization and our method is more robust than competing methods on similar datasets.The proposed matching error computed between the transformed patch and the map clearly indicates whether a reliable pose estimate has been obtained.

Place, publisher, year, edition, pages
IEEE , 2013. 88-95 p.
Keyword [en]
Pose estimation, aerial images, registration, 3D model
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-89477DOI: 10.1109/WORV.2013.6521919ISI: 000325279400014ISBN: 978-1-4673-5646-6 (print)ISBN: 978-1-4673-5647-3 (print)OAI: oai:DiVA.org:liu-89477DiVA: diva2:607988
Conference
IEEE Workshop on Robot Vision 2013, Clearwater Beach, Florida, USA, January 16-17, 2013
Available from: 2013-02-26 Created: 2013-02-26 Last updated: 2016-05-04
In thesis
1. Global Pose Estimation from Aerial Images: Registration with Elevation Models
Open this publication in new window or tab >>Global Pose Estimation from Aerial Images: Registration with Elevation Models
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Over the last decade, the use of unmanned aerial vehicles (UAVs) has increased drastically. Originally, the use of these aircraft was mainly military, but today many civil applications have emerged. UAVs are frequently the preferred choice for surveillance missions in disaster areas, after earthquakes or hurricanes, and in hazardous environments, e.g. for detection of nuclear radiation. The UAVs employed in these missions are often relatively small in size which implies payload restrictions.

For navigation of the UAVs, continuous global pose (position and attitude) estimation is mandatory. Cameras can be fabricated both small in size and light in weight. This makes vision-based methods well suited for pose estimation onboard these vehicles. It is obvious that no single method can be used for pose estimation in all dierent phases throughout a ight. The image content will be very dierent on the runway, during ascent, during  ight at low or high altitude, above urban or rural areas, etc. In total, a multitude of pose estimation methods is required to handle all these situations. Over the years, a large number of vision-based pose estimation methods for aerial images have been developed. But there are still open research areas within this eld, e.g. the use of omnidirectional images for pose estimation is relatively unexplored.

The contributions of this thesis are three vision-based methods for global egopositioning and/or attitude estimation from aerial images. The rst method for full 6DoF (degrees of freedom) pose estimation is based on registration of local height information with a geo-referenced 3D model. A dense local height map is computed using motion stereo. A pose estimate from navigation sensors is used as an initialization. The global pose is inferred from the 3D similarity transform between the local height map and the 3D model. Aligning height information is assumed to be more robust to season variations than feature matching in a single-view based approach.

The second contribution is a method for attitude (pitch and roll angle) estimation via horizon detection. It is one of only a few methods in the literature that use an omnidirectional (sheye) camera for horizon detection in aerial images. The method is based on edge detection and a probabilistic Hough voting scheme. In a  ight scenario, there is often some knowledge on the probability density for the altitude and the attitude angles. The proposed method allows this prior information to be used to make the attitude estimation more robust.

The third contribution is a further development of method two. It is the very rst method presented where the attitude estimates from the detected horizon in omnidirectional images is rened through registration with the geometrically expected horizon from a digital elevation model. It is one of few methods where the ray refraction in the atmosphere is taken into account, which contributes to the highly accurate pose estimates. The attitude errors obtained are about one order of magnitude smaller than for any previous vision-based method for attitude estimation from horizon detection in aerial images.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 53 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1672
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-108213 (URN)10.3384/lic.diva-108213 (DOI)978-91-7519-279-6 (ISBN)
Presentation
2014-08-22, Visionen, B-huset, Campus Valla, Linköpings universitet, Linköping, 13:15 (Swedish)
Opponent
Supervisors
Available from: 2014-06-26 Created: 2014-06-26 Last updated: 2016-05-04Bibliographically approved

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