Advertisement

Least-square Matching for Mobile Robot SLAM Based on Line-segment Model

  • Sang-Hyung Park
  • Soo-Yeong YiEmail author
Article
  • 15 Downloads

Abstract

This study proposes an efficient simultaneous localization and mapping (SLAM) algorithm for a mobile robot. The proposed algorithm consists of line-segment feature extraction from a set of points measured by a LIDAR, association and matching between the line-segments and a map database for position estimation, and the registration of the line-segments into the map database for the incremental construction of the map database. The line-segment features help reduce the amount of data required for map representation. The matching algorithm for position estimation is efficient in computation owing to the use of a number of inliers as the weights in the least-squares method. Experiments are conducted to demonstrate the performance of the proposed SLAM algorithm, and the results show that the proposed algorithm is effective in the map representation and the localization of a mobile robot.

Keywords

Line-segment localization map-making mobile robot SLAM 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Y. Kanayama, Y. Kimura, F. Miyazaki, and T. Noguchi, “A stable tracking control method for an autonomous mobile robot,” Proc. of IEEE Int’l Conf. on Robotics and Automation, Cincinnati, USA, May 1990.Google Scholar
  2. [2]
    M. Sualeh and G. Kim, “Simultaneous localization and mapping in the epoch of semantics: a survey,” International Journal of Control, Automation and Systems, vol. 17, no. 3, pp. 729–742, 2019.CrossRefGoogle Scholar
  3. [3]
    I. Cox, “Blanche-an experiment in guidance and navigation of an autonomous robot vehicle,” IEEE Trans. on Robotics and Automation, vol. 7, no. 2, pp. 193–204, 1991.MathSciNetCrossRefGoogle Scholar
  4. [4]
    I. Cox and J. Kruskal, “On the congruence of noisy images to line segment models,” Proc. of Int’l Conf. on Computer Vision, pp. 252–258, 1988.Google Scholar
  5. [5]
    J. Leonard and H. Durrant-Whyte, “Simultaneous map building and localization for an autonomous mobile robot,” Proc. of IEEE/RSJ Int’l Workshop on Intelligent Robots and Systems (IROS’91), pp. 1442–1447, 1991.CrossRefGoogle Scholar
  6. [6]
    M. Jung and J. Song, “Efficient autonomous global localization for service robots using dual laser scanners and rotational motion,” International Journal of Control, Automation and Systems, vol. 15, no. 2, pp. 743–751, 2017.CrossRefGoogle Scholar
  7. [7]
    S. Riisgard and M. Blas, SLAM for Dummies: A Tutorial Approach to Simultaneous Localizing and Mapping, MIT, 2004.Google Scholar
  8. [8]
    A. Davison, I. Reid, and N. Molton, “MonoSLAM: realtime single camera SLAM,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052–1067, Apr. 2007.CrossRefGoogle Scholar
  9. [9]
    N. Karlsson, E. Bernardo, and J. Ostrowski, “The vSLAM algorithm for robust localization and mapping,” Proc. of IEEE Int’l Conf. on Robotics and Automation, Barcelona, Spain, Jan. 2006.Google Scholar
  10. [10]
    J. Sturm, N. Engelhard, and F. Endres, “A benchmark for the evaluation of RGB-D SLAM systems,” Proc. of IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, Vilamoura, Portugal, Dec. 2012.Google Scholar
  11. [11]
    Y. Li and E. Olson, “Extracting general-purpose features from LIDAR data,” Proc. of IEEE Int’l Conf. on Robotics and Automation, Anchorage, AK, USA, May 2010.Google Scholar
  12. [12]
    L. Pedraza, D. Rodriguez-Losada, F. Matia, G. Dissanayake, and J. Miro, “Extending the limits of feature-based SLAM with b-splines,” IEEE Trans. on Robotics, vol. 25, no. 2, pp. 353–366, April 2009.CrossRefGoogle Scholar
  13. [13]
    P. Nunez, R. Vazquez-Martin, J. del Toro, A. Bandera, and F. Sandoval, “Feature extraction from laser scan data based on curvature estimation for mobile robotics,” Proc. of IEEE Int’l Conf. on Robotics and Automation, pp. 1167–1172, May 2006.Google Scholar
  14. [14]
    Y. Li and E. Olson, “Structure tensors for general purpose LIDAR feature extraction,” Proc. of IEEE Int’l Conf. on Robotics and Automation (ICRA), Shanghai, China, Aug. 2011.Google Scholar
  15. [15]
    A. Garulli, A. Giannitrapani, A. Rossi, and A. Vicino, “Mobile robot SLAM for line-based environment representation,” Proc. of Decision and Control, and European Control Conference. CDC-ECC’ 05. 44th IEEE Conf., Seville, Spain, Dec. 2005.Google Scholar
  16. [16]
    S. Yi and B. Choi, “Autonomous navigation of indoor mobile robots using a global ultrasonic system,” Robotica, vol. 22, pp. 369–374, 2004.CrossRefGoogle Scholar
  17. [17]
    J. Leonard and H. Durrant-whyte, “Simultanenous map building and localization for an autonomous mobile robot,” Proc. of IEEE/RSJ Int’l Workshop on Intelligent Robots and Systems (IROS’91), pp. 1442–1447, 1991.CrossRefGoogle Scholar
  18. [18]
    H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part I,” IEEE Robotics & Automation Magazine, vol. 13, no. 3, pp. 99–108, June 2006.CrossRefGoogle Scholar
  19. [19]
    H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part II,” IEEE Robotics & Automation Magazine, vol. 13, no. 3, pp. 108–117, Sept. 2006.CrossRefGoogle Scholar
  20. [20]
    M. Montelmerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: a factored solution to the simultaneous localization and mapping problem,” Proc. of the AAAI National Conf. on Artificial Intelligence, USA, 2002.Google Scholar
  21. [21]
    D. Hahnel, W. Burgard, and D. Fox, and S. Thrun, “An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements,” Proc. of IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, Oct. 2003.Google Scholar
  22. [22]
    K. Derpanis, “Overview of the RANSAC algorithm,” Technical Report, Computer Science, York University, 2010.Google Scholar
  23. [23]
  24. [24]
    R. Kummerle, B. Steder, C. Dornhege, M. Ruhnke, G. Grisetti, C. Stachniss, and A. Kleiner, “On measuring the accuracy of SLAM algorithms,” Autonomous Robot, vol. 27, pp. 387–407, 2009.CrossRefGoogle Scholar
  25. [25]
  26. [26]
    B. Williams, M. Cummins, J. Neira, P. Newman, I. Reid, and J. Tardós, “A comparison of loop closing techniques in monocular SLAM,” Robot. Auton. Syst., vol. 57, no. 12, pp. 1188–1197, 2009.CrossRefGoogle Scholar
  27. [27]
    G. Grisetti, R. Kümmerle, C. Stachniss, and W. Burgard, “A tutorial on graph-based SLAM,” IEEE Intell. Transp. Syst. Mag., vol. 2, no. 4, pp. 31–43, 2010.CrossRefGoogle Scholar

Copyright information

© ICROS, KIEE and Springer 2019

Authors and Affiliations

  1. 1.Department of Electrical and Information EngineeringSeoul National University of Science and TechnologySeoulKorea

Personalised recommendations