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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, p. 88-95Conference 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. p. 88-95
Keywords [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, id: 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: 2019-04-12
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. p. 53
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: 2018-01-11Bibliographically approved
2. Vision-based Localization and Attitude Estimation Methods in Natural Environments
Open this publication in new window or tab >>Vision-based Localization and Attitude Estimation Methods in Natural Environments
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the last decade, the usage of unmanned systems such as Unmanned Aerial Vehicles (UAVs), Unmanned Surface Vessels (USVs) and Unmanned Ground Vehicles (UGVs) has increased drastically, and there is still a rapid growth. Today, unmanned systems are being deployed in many daily operations, e.g. for deliveries in remote areas, to increase efficiency of agriculture, and for environmental monitoring at sea. For safety reasons, unmanned systems are often the preferred choice for surveillance missions in hazardous environments, e.g. for detection of nuclear radiation, and in disaster areas after earthquakes, hurricanes, or during forest fires. For safe navigation of the unmanned systems during their missions, continuous and accurate global localization and attitude estimation is mandatory.

Over the years, many vision-based methods for position estimation have been developed, primarily for urban areas. In contrast, this thesis is mainly focused on vision-based methods for accurate position and attitude estimates in natural environments, i.e. beyond the urban areas. Vision-based methods possess several characteristics that make them appealing as global position and attitude sensors. First, vision sensors can be realized and tailored for most unmanned vehicle applications. Second, geo-referenced terrain models can be generated worldwide from satellite imagery and can be stored onboard the vehicles. In natural environments, where the availability of geo-referenced images in general is low, registration of image information with terrain models is the natural choice for position and attitude estimation. This is the problem area that I addressed in the contributions of this thesis.

The first contribution is a method for full 6DoF (degrees of freedom) pose estimation from aerial images. A dense local height map is computed using structure from motion. The global pose is inferred from the 3D similarity transform between the local height map and a digital elevation model. Aligning height information is assumed to be more robust to season variations than feature-based matching.

The second contribution is a method for accurate attitude (pitch and roll angle) estimation via horizon detection. It is one of only a few methods that use an omnidirectional (fisheye) camera for horizon detection in aerial images. The method is based on edge detection and a probabilistic Hough voting scheme. The method allows prior knowledge of the attitude angles to be exploited to make the initial attitude estimates more robust. The estimates are then refined through registration with the geometrically expected horizon line from a digital elevation model. To the best of our knowledge, it is the first method where the ray refraction in the atmosphere is taken into account, which enables the highly accurate attitude estimates.

The third contribution is a method for position estimation based on horizon detection in an omnidirectional panoramic image around a surface vessel. Two convolutional neural networks (CNNs) are designed and trained to estimate the camera orientation and to segment the horizon line in the image. The MOSSE correlation filter, normally used in visual object tracking, is adapted to horizon line registration with geometric data from a digital elevation model. Comprehensive field trials conducted in the archipelago demonstrate the GPS-level accuracy of the method, and that the method can be trained on images from one region and then applied to images from a previously unvisited test area.

The CNNs in the third contribution apply the typical scheme of convolutions, activations, and pooling. The fourth contribution focuses on the activations and suggests a new formulation to tune and optimize a piecewise linear activation function during training of CNNs. Improved classification results from experiments when tuning the activation function led to the introduction of a new activation function, the Shifted Exponential Linear Unit (ShELU).

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 81
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1977
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-154159 (URN)10.3384/diss.diva-154159 (DOI)9789176851180 (ISBN)
Public defence
2019-04-26, Domen, Visualiseringscenter C, Campus Norrköping, Norrköping, 13:15 (English)
Opponent
Supervisors
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2019-04-12 Created: 2019-04-09 Last updated: 2019-04-30Bibliographically approved

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Grelsson, BertilFelsberg, Michael

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