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Micro air vehicle localization and position tracking from textured 3D cadastral models


Majdik, Andras; Verda, Damiano; Albers-Schoenberg, Yves; Scaramuzza, Davide (2014). Micro air vehicle localization and position tracking from textured 3D cadastral models. In: IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 31 May 2014 - 7 June 2014, 920-927.

Abstract

In this paper, we address the problem of localizing a camera-equipped Micro Aerial Vehicle (MAV) flying in urban streets at low altitudes. An appearance-based global positioning system to localize MAVs with respect to the surrounding buildings is introduced. We rely on an air-ground image matching algorithm to search the airborne image of the MAV within a ground-level Street View image database and to detect image matching points. Based on the image matching points, we infer the global position of the MAV by back-projecting the corresponding image points onto a cadastral 3D city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry, whenever a good match is detected between the airborne MAV and the street-level images. The proposed approach is tested on a dataset captured with a small quadroctopter flying in the streets of Zurich.

Abstract

In this paper, we address the problem of localizing a camera-equipped Micro Aerial Vehicle (MAV) flying in urban streets at low altitudes. An appearance-based global positioning system to localize MAVs with respect to the surrounding buildings is introduced. We rely on an air-ground image matching algorithm to search the airborne image of the MAV within a ground-level Street View image database and to detect image matching points. Based on the image matching points, we infer the global position of the MAV by back-projecting the corresponding image points onto a cadastral 3D city model. Furthermore, we describe an algorithm to track the position of the flying vehicle over several frames and to correct the accumulated drift of the visual odometry, whenever a good match is detected between the airborne MAV and the street-level images. The proposed approach is tested on a dataset captured with a small quadroctopter flying in the streets of Zurich.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:7 June 2014
Deposited On:12 Aug 2016 07:33
Last Modified:13 Aug 2016 08:01
Publisher:Institute of Electrical and Electronics Engineers
Series Name:IEEE International Conference on Robotics and Automation. Proceedings
ISSN:1050-4729
Publisher DOI:https://doi.org/10.1109/ICRA.2014.6906964
Related URLs:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6906964 (Publisher)
http://www.icra2014.com/ (Organisation)
Other Identification Number:merlin-id:10211

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