Georeferencing of an Unmanned Aerial System by Means of an Iterated Extended Kalman Filter Using a 3D City Model

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Bureick, J.; Vogel, S.; Neumann, I.; Unger, J.; Alkhatib, H.: Georeferencing of an Unmanned Aerial System by Means of an Iterated Extended Kalman Filter Using a 3D City Model. In: PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science 87 (2019), S. 229-247. DOI:

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Sum total of downloads: 130

In engineering geodesy, the technical progress leads to various kinds of multi-sensor systems (MSS) capturing the environment. Multi-sensor systems, especially those mounted on unmanned aerial vehicles, subsequently called unmanned aerial system (UAS), have emerged in the past decade. Georeferencing for MSS and UAS is an indispensable task to obtain further products of the data captured. Georeferencing comprises at least the determination of three translations and three rotations. The availability and accuracy of Global Navigation Satellite System (GNSS) receivers, inertial measurement units, or other sensors for georeferencing is not or not constantly given in urban scenarios. Therefore, we utilize UAS-based laser scanner measurements on building facades. The building latter are modeled as planes in a three-dimensional city model. We determine the trajectory of the UAS by combining the laser scanner measurements with the plane parameters. The resulting implicit measurement equations and nonlinear equality constraints are covered within an iterated extended Kalman filter (IEKF). We developed a software simulation for testing the IEKF using different scenarios to evaluate the functionality, performance, strengths, and remaining challenges of the IEKF implemented.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2019
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 84 64.62%
2 image of flag of United States United States 24 18.46%
3 image of flag of China China 5 3.85%
4 image of flag of Russian Federation Russian Federation 3 2.31%
5 image of flag of Canada Canada 3 2.31%
6 image of flag of No geo information available No geo information available 2 1.54%
7 image of flag of France France 2 1.54%
8 image of flag of Europe Europe 1 0.77%
9 image of flag of Czech Republic Czech Republic 1 0.77%
10 image of flag of Austria Austria 1 0.77%
    other countries 4 3.08%

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