Evaluation of Automatically Extracted Landmarks for Future Driver Assistance Systems

Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

In the future, vehicles will gather more and more spatial information about their environment, using on-board sensors such as cameras and laser scanners. Using this data, e.g. for localization, requires highly accurate maps with a higher level of detail than provided by todays maps. Producing those maps can only be realized economically if the information is obtained fully automatically. It is our goal to investigate the creation of intermediate level maps containing geo-referenced landmarks, which are suitable for the specific purpose of localization. To evaluate this approach, we acquired a dense laser scan of a 22 km scene, using a mobile mapping system. From this scan, we automatically extracted pole-like structures, such as street and traffic lights, which form our pole database. To assess the accuracy, ground truth was obtained for a selected inner-city junction by a terrestrial survey. In order to evaluate the usefulness of this database for localization purposes, we obtained a second scan, using a robotic vehicle equipped with an automotive-grade laser scanner. We extracted poles from this scan as well and employed a local pole matching algorithm to improve the vehicles position.

Keywords

Global Position System Point Cloud Real Time System Traffic Light Driver Assistance System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Matthias Hentschel from the Real Time Systems Group at the Institute for Systems Engineering of the Leibniz Universität Hannover for providing the RTS Hanna scans.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Cartography and GeoinformaticsLeibniz Universität HannoverHannoverGermany

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