Unsupervised Calibration for Multi-beam Lasers

  • Jesse LevinsonEmail author
  • Sebastian Thrun
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)


Light Detection and Ranging (LIDAR) sensors have become increasingly common in both industrial and robotic applications. LIDAR sensors are particularly desirable for their direct distance measurements and high accuracy, but traditionally have been configured with only a single rotating beam. However, recent technological progress has spawned a new generation of LIDAR sensors equipped with many simultaneous rotating beams at varying angles, providing at least an order of magnitude more data than single-beam LIDARs and enabling new applications in mapping [6], object detection and recognition [15], scene understanding [16], and SLAM [9].


Point Cloud Inertial Measurment Unit Iterate Close Point Horizontal Angle Intensity Return 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chao, G., Spletzer, J.: On-Line Calibration of Multiple LIDARs on a Mobile Vehicle Platform. In: ICRA 2010 (2010)Google Scholar
  2. 2.
    Censi, A., Marchionni, L., Oriolo, G.: Simultaneous maximum-likelihood calibration of odometry and sensor parameters. In: ICRA 2008 (2008)Google Scholar
  3. 3.
    Kaboli, A., Bowling, M., Musilek, P.: Bayesian calibration for Monte Carlo localization. In: AAAI 2006 (2006)Google Scholar
  4. 4.
    Muhammad, N., Lacroix, S.: Calibration of a rotating multi-beam Lidar (2009),
  5. 5.
    Mount, D., Arya, S.: ANN: A Library for Approximate Nearest Neighbor Searching,
  6. 6.
    Levinson, J., Thrun, S.: Robust Vehicle Localization in Urban Environments Using Probabilistic Maps. In: ICRA 2010 (2010)Google Scholar
  7. 7.
    Chen, Y., Medioni, G.: Object Modeling by Registration of Multiple Range Images. In: Proc. of the 1992 IEEE Intl. Conf. on Robotics and Automation, pp. 2724–2729 (1991)Google Scholar
  8. 8.
    Dempster, A., Laird, P., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B 39(1), 1–38Google Scholar
  9. 9.
    Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. In: Robotics Science and Systems (2009)Google Scholar
  10. 10.
    Underwood, J., Hill, A., Scheding, S.: Calibration of range sensor pose on mobile platforms. In: Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA (October 2007)Google Scholar
  11. 11.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)Google Scholar
  12. 12.
    Lee, K., Kalyan, B., Wijesoma, S., Adams, M., Hover, F., Patrikalakis, N.: Tracking random finite objects using 3D-LIDAR in marine environments. In: Proceedings of the 2010 ACM Symposium on Applied Computing (2010)Google Scholar
  13. 13.
    Shackleton, J., VanVoorst, B., Hesch, J.: Tracking People with a 360-degree Lidar. In: 7th IEEE Conference on Advanced Video and Signal Based Surveillance (2010)Google Scholar
  14. 14.
    Petrovskaya, A., Thrun, S.: Model based vehicle detection and tracking for autonomous urban driving. Autonomous Robots 26(2-3) (April 2009)Google Scholar
  15. 15.
    Douillard, B., Brooks, A., Ramos, F.: A 3D Laser and Vision Based Classifier. In: International Conference in Intelligent Sensors, Sensor Networks and Information Professing, ISSNIP 2009 (2009)Google Scholar
  16. 16.
    Steinhauser, D., Ruepp, O., Burschka, D.: Motion segmentation and scene classification from 3D LIDAR data. In: Intelligent Vehicles Symposium. IEEE (2008)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Stanford UniversityStanfordUSA

Personalised recommendations