JRM Vol.22 No.2 pp. 158-166
doi: 10.20965/jrm.2010.p0158


6-DOF Localization for a Mobile Robot Using Outdoor 3D Point Clouds

Taro Suzuki, Yoshiharu Amano, and Takumi Hashizume

Research Institute for Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, Tokyo 162-0044, Japan

September 30, 2009
January 8, 2010
April 20, 2010
mobile robot, localization, particle filter, 3D map, MCL

This paper describes outdoor localization for a mobile robot using a laser scanner and three-dimensional (3D) point cloud data. A Mobile Mapping System (MMS) measures outdoor 3D point clouds easily and precisely. The full six-dimensional state of a mobile robot is estimated combining dead reckoning and 3D point cloud data. Two-dimensional (2D) position and orientation are extended to 3D using 3D point clouds assuming that the mobile robot remains in continuous contact with the road surface. Our approach applies a particle filter to correct position error in the laser measurement model in 3D point cloud space. Field experiments were conducted to evaluate the accuracy of our proposal. As the result of the experiment, it was confirmed that a localization precision of 0.2 m (RMS) is possible using our proposal.

Cite this article as:
Taro Suzuki, Yoshiharu Amano, and Takumi Hashizume, “6-DOF Localization for a Mobile Robot Using Outdoor 3D Point Clouds,” J. Robot. Mechatron., Vol.22, No.2, pp. 158-166, 2010.
Data files:
  1. [1] S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics,” The MIT Press, 2005.
  2. [2] F. Dellaert et al., “Monte Carlo Localization for Mobile Robots,” In Proc. of IEEE ICRA, pp. 1322-1328, 1999.
  3. [3] F. Lu and E. Milios, “Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans,” J. of Intelligent and Robotic Systems, Vol.18 No.3, pp. 249-275, 1997.
  4. [4] D. Fox et al., “Monte Carlo Localization: Efficient Position Estimation for Mobile Robots,” In Proc. of AAAI, pp. 343-349, 1999.
  5. [5] J. Meguro, R. Hirokawa, J. Takiguchi, and T. Hashizume, “Autonomous Mobile Surveillance System based on RTK-GPS in Urban Canyons,” J. of Robotics and Mech. (JRM) No.17, Vol.2, pp. 218-225, 2005.
  6. [6] R. Hirokawa et al., “An Efficient UKF Based GPS/INS Augmented by Local Landmark Update,” Proc. ION GNSS, pp. 127-134, 2007.
  7. [7] S. Thrun et al., “Stanley, the robot that won the DARPA Grand Challenge,” J. of Field Robotics, Vol.23, No.9, pp. 661-692, 2006.
  8. [8] D. Barber, J. Mills, and S. Smith-Voysey, “Geometric validation of a ground-based mobile laser scanning system,” ISPRS J. of Photogrammetry and Remote Sensing, Vol.63, No.1, pp. 128-141, 2008.
  9. [9] K. Ishikawa et al., “A Mobile Mapping System for Precise Road Line Localization Using Single Camera and 3D Road Model,” J. of Robotics and Mech. (JRM) Vol.19, No.2, pp. 174-180, 2007.
  10. [10] J. Kremer and G. Hunter, “Performance of the Street Mapper Mobile LIDAR Mapping System in “Real World” Projects,” Photogrammetric Week ’07, pp. 215-225, 2007.
  11. [11] K. Lingemann, A. Nuchter, J. Hertzberg, and H. Surmann, “Highspeed laser localization for mobile robots,” Robotics and Autonomous Systems, Vol.51, pp. 275-296, 2005.
  12. [12] A. Nüuchter et al., “6D SLAM – 3D Mapping Outdoor Environments, Quantitative Performance Evaluation of Robotic and Intelligent Systems,” J. of Field Robotics Vol.24, No.8-9, pp. 699-722, 2007.
  13. [13] A. Davison et al., “Monoslam: Real-time single camera slam,” IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 1052-1067, 2007.
  14. [14] E. B. Olson, “Real-time correlative scan matching,” Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 4387-4393, 2009.
  15. [15] T. Duckett and U. Nehmzow, “Mobile Robot Self-Localisation Using Occupancy Histograms and A Mixture of Gaussian Location Hypotheses,” J. Robotics and Autonomous Systems, Vol.34, No.2-3, pp. 119-130, 2001.
  16. [16] S. Thrun et al., “Robust Monte Carlo Localization for Mobile Robots,” Artificial Intelligence Journal, Vol.128, No.1-2, pp. 99-141, 2001.
  17. [17] R. Küummerle et al. “Monte Carlo Localization in Outdoor Terrains Using Multilevel Surface Maps,” J. of Field Robotics Vol.25, No.6-7, pp. 346-359, 2008.
  18. [18] K. Lingemann et al., “High-speed laser localization for mobile robots,” J. of Robotics and Autonomous Systems, 51, No.4, pp. 275-296, 2005.
  19. [19] S. Thrun et al., “A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping,” in Proc. of the IEEE Int. Conf. on Robotics and Automation, 2000.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 01, 2021