Evaluation of Automatically Extracted Landmarks for Future Driver Assistance Systems

  • Claus Brenner
  • Sabine Hofmann
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


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.


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.



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.


  1. Borrmann D, Elseberg J, Lingemann K, Nüchter A, Hertzberg J (2008) Globally consistent 3D mapping with scan matching. J Robot Auton Syst 56(2):130–142CrossRefGoogle Scholar
  2. Burgard W, Hebert M (2008) World modeling. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer, Heidelberg, Germany, pp 853–869CrossRefGoogle Scholar
  3. EDMap (2004) Enhanced digital mapping project final report. Technical report, United States department of transportation, federal highway administration and national highway traffic and safety administration. Accessed 14 July 2011
  4. IBEO (2009) Ibeo Lux laser scanner. Accessed 14 July 2011
  5. Kremer J, Hunter G (2007) Performance of the streetmapper mobile lidar mapping system in real world projects. In: Fritsch D (Ed.) Photogrammetric Week 2007. Wichmann, Heidelberg, pp 215–225Google Scholar
  6. Kukko A, Andrei C-O, Salminen V-M, Kaartinen H, Chen Y, Rönnholm P, Hyyppä H, Hyyppä J, Chen R, Haggrén H, Kosonen I, Capek K (2007) Road environment mapping system of the finnish geodetic institute – FGI roamer. In: IAPRS (ed.), Proceedings of laser scanning 2007 and silviLaser 2007, Vol. 36 Part 3/W 52, pp 241–247Google Scholar
  7. Pandazis J-C (2002) NextMAP: investigating the future of digital map databases. In: e-Safety congress, Lyon. Accessed 14 July 2011
  8. Rabbani T, Dijkman S, van den Heuvel F, Vosselman G (2007) An integrated approach for modeling and global registration of point clouds. ISPRS J Photogramm Remote Sens 61(6):355–370CrossRefGoogle Scholar
  9. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. The MIT Press, Cambridge, MAGoogle Scholar
  10. Weiss T, Kaempchen N, Dietmayer K (2005) Precise ego localization in urban areas using laserscanner and high accuracy feature maps. In: Proceedings of 2005 IEEE intelligent vehicles symposium, Las Vegas, USA, pp 284–289Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of Cartography and GeoinformaticsLeibniz Universität HannoverHannoverGermany

Personalised recommendations