Skip to main content
Log in

An Improved Method for Extrinsic Calibration of Tilting 2D LRF

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper proposes an improved calibration method to accurately estimate extrinsic calibration parameters of a tilting 2D Laser Range Finder (LRF). Tilting 2D LRF (a low cost 3D scanner device) with a unidirectional rotating platform has been widely used in robotics applications to scan the 3D environment. Ideally, the tilt axis of rotating mechanism should pass through the optical rotation centre of the 2D LRF. However, due to misalignment during assembling of 2D LRF with the rotating platform, the centres of rotation may not coincide with each other. Though, the system must be calibrated to align both the centres of rotation so as to improve the accuracy in building 3D point cloud of the environment. Unlike the previous calibration techniques, the main advantage of the proposed method is that it accurately estimates all 6-DOF calibration parameters, especially rotation and translation calibration parameters along motor rotation axis between 2D LRF and rotating platform without any additional hardware, camera or rolling/bidirectional rotation mechanism. The proposed method utilizes the normal vector to the calibration board plane and coordinates of laser points at the endpoints of the extracted calibration board line in the 2D scan to obtain these remaining calibration parameters. The obtained parameters are then refined using Levenberg-Marquardt non-linear optimization algorithm. The performance of the algorithm is validated on a range of real as well as synthetic data and the estimated parameters with the proposed approach exhibits 24.54% reduction in RMS (root mean square) error as compared to the conventional approach. Furthermore, qualitative and quantitative analysis shows that the proposed method produces accurate results and is able to reduce the artifacts along the rotation axis in the 3D point cloud which usually appear in the point cloud obtained from the conventional approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ye, C.: Navigating a mobile robot by a traversability field histogram. IEEE Trans. Syst. Man Cybern. B Cybern. 37(2), 361–372 (2007)

    Article  Google Scholar 

  2. Birk, A., Vaskevicius, N., Pathak, K., Schwertfeger, S., Poppinga, J., Buelow, H.: 3-D perception and modelling. IEEE Robot. Autom. Mag. 16(4), 53–60 (2009)

    Article  Google Scholar 

  3. Cho, S.H., Hong, S.: Map based indoor robot navigation and localization using laser range finder. In: Proc of the 11th IEEE International Conference on Control Automation Robotics & Vision (ICARCV), pp. 1559–1564 (2010)

    Google Scholar 

  4. Kim, J., Chung, W.: Localization of a mobile robot using a laser range finder in a glass-walled environment. IEEE Trans. Ind. Electron. 63(6), 3616–3627 (2016)

    Article  Google Scholar 

  5. Liang, Z., Zhu, S., Fang, F., Jin, X.: Simultaneous Localization and Mapping in a Hybrid Robot and Camera Network System. J. Intell. Robot. Syst. 1–24 (2010). https://doi.org/10.1007/s10846-010-9446-3

  6. Aghili, F., Su, C.Y.: Robust relative navigation by integration of ICP and adaptive Kalman filter using laser scanner and IMU. IEEE/ASME Trans. Mechatron. 21(4), 2015–2026 (2016)

    Article  Google Scholar 

  7. Singh, R., Nagla, K.S.: Improved 2D laser grid mapping by solving mirror reflection uncertainty in SLAM. International Journal of Intelligent Unmanned Systems. 6(2), 93–114 (2018)

    Article  Google Scholar 

  8. Singh, R., Nagla, K.S.: Error analysis of laser scanner for robust autonomous navigation of mobile robot in diverse illumination environment. World Journal of Engineering. 15(5), 626–632 (2018)

    Google Scholar 

  9. Kurisu, M., Muroi, H., Yokokohji, Y., Kuwahara, H.: Development of a laser range finder for 3d map-building in rubble; installation in a rescue robot. In: Proc of the IEEE International Conference on Mechatronics and Automation (ICMA), pp. 2054–2059 (2007)

    Google Scholar 

  10. Singh, R., Nagla, K.S.: Multi-data sensor fusion framework to detect transparent object for the efficient mobile robot mapping. International Journal of Intelligent Unmanned Systems. 7(1), 2–18 (2019)

    Article  Google Scholar 

  11. Jung, E.J., Lee, J.H., Yi, B.J., Park, J., Yuta, S.I., Noh, S.T.: Development of a laser-range-finder-based human tracking and control algorithm for a marathoner service robot. IEEE/ASME Trans. Mechatron. 19(6), 1963–1976 (2014)

    Article  Google Scholar 

  12. User’s Manual and Programming Guide HDL-64E S, Velodyne, Morgan Hill, CA, USA (2013). www.velodynelidar.com

  13. RobotEye RE08 3D-LiDAR 3D Laser Scanning System Product Datasheet, Ocular Robotics, Kingsgrove, Australia (2015). www.ocularrobotics.com

  14. Khurana, A., Nagla, K.S.: Signal averaging for noise reduction in Mobile robot 3D measurement system. MAPAN. 33(1), 33–41 (2018)

    Article  Google Scholar 

  15. Kang, J., Doh, N.L.: Full-DOF calibration of a rotating 2-D LIDAR with a simple plane measurement. IEEE Trans. Robot. 32(5), 1245–1263 (2016)

    Article  Google Scholar 

  16. Gao, Z., Huang, J., Yang, X., An, P.: Calibration of rotating 2D LIDAR based on simple plane measurement. Sens. Rev. 39(2), 190–198 (2019)

    Article  Google Scholar 

  17. Wai Yan So, E., Basso, F., Menegatti, E.: Calibration of a rotating 2D laser range-finder using point plane constraints. Journal of Automation, Mobile Robotics and Intelligent Systems. 7(2), 30–39 (2013)

    Google Scholar 

  18. Pradeep, V., Konolige, K., Berger, E.: Calibrating a multi-arm multi-sensor robot: A bundle adjustment approach. In: Proc of the 12th International Symposium on Experimental robotics, pp. 211–225, Berlin, Heidelberg (2014)

  19. Kurnianggoro, L., Hoang, V.D., Jo, K.H.: Calibration of a 2D laser scanner system and rotating platform using a point-plane constraint. Comput. Sci. Inf. Syst. 12(1), 307–322 (2015)

    Article  Google Scholar 

  20. Olivka, P., Krumnikl, M., Moravec, P., Seidl, D.: Calibration of short range 2D laser range finder for 3D SLAM usage. Journal of Sensors. (2016). https://doi.org/10.1155/2016/3715129

  21. Sheehan, M., Harrison, A., Newman, P.: Self-calibration for a 3D laser. Int. J. Robot. Res. 31(5), 675–687 (2012)

    Article  Google Scholar 

  22. Oberlaender, J., Pfotzer, L., Roennau, A., Dillmann, R.: Fast calibration of rotating and swivelling 3-D laser scanners exploiting measurement redundancies. In: Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3038–3044 (2015)

    Google Scholar 

  23. Alismail, H., Browning, B.: Automatic calibration of spinning actuated lidar internal parameters. J. Field Rob. 32(5), 723–747 (2015)

    Article  Google Scholar 

  24. Zeng, Y., Yu, H., Dai, H., Song, S., Lin, M., Sun, B., Jiang, W., Meng, M.: An improved calibration method for a rotating 2D LiDAR system. Sensors. 18(2), 497 (2018)

    Article  Google Scholar 

  25. Yamao, S., Hidaka, H., Odashima, S., Jiang, S., Murase, Y.: Calibration of a rotating 2D LRF in unprepared environments by minimizing redundant measurement errors. In: Proc of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 172–177 (2017)

    Chapter  Google Scholar 

  26. Lourenço, B., Oliveira, P., Oliveira, M.: Extrinsic Calibration of 2D Laser Range Finders using Planar Features. In: Proc of 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 1–6 (2019)

    Google Scholar 

  27. Lin, C.C., Liao, Y.D., Luo, W.J.: Calibration method for extending single-layer LIDAR to multi-layer LIDAR. In: Proc of the IEEE/SICE International Symposium on System Integration, pp. 677–681 (2013)

    Chapter  Google Scholar 

  28. Morales, J., Martínez, J.L., Mandow, A., Reina, A.J., Pequeño-Boter, A., García-Cerezo, A.: Boresight calibration of construction misalignments for 3D scanners built with a 2D laser rangefinder rotating on its optical center. Sensors. 14(11), 20025–20040 (2014)

    Article  Google Scholar 

  29. Li, G., Li, D., Pan, L., Henghai, F.: The calibration algorithm of a 3D color measurement system based on the line feature. International Journal of Image, Graphics and Signal Processing. 1(1), 17 (2009)

    Article  Google Scholar 

  30. Yang, G., Zhengchun, D., Zhenqiang, Y.: Calibration method of three dimensional (3D) laser measurement system based on projective transformation. In: Proc of the 3rd IEEE International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), pp. 666–671 (2011)

    Google Scholar 

  31. Katuwandeniya, K., Ranasinghe, R., Dantanarayana, L., Dissanayake, G., Liu, D.: Calibration of a Rotating Laser Range Finder using Intensity Features. In: Proc of the 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 228–234 (2018)

    Chapter  Google Scholar 

  32. Huang, C.M., Tseng, Y.H.: Plane fitting methods of LIDAR point cloud. In: Proc of the 29th Asian Conference on Remote Sensing (ACRS), pp. 1925–1930 (2008)

    Google Scholar 

  33. Zhao, R., Fan, J.: Global complexity bound of the Levenberg–Marquardt method. Optim. Methods and Softw. 31(4), 805–814 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  34. Ye, C., Borenstein, J.: Characterization of a 2D laser scanner for mobile robot obstacle negotiation. In: Proc of the IEEE International Conference on Robotics and Automation, pp. 2512–2518 (2002)

    Google Scholar 

  35. Yeon, S., Jun, C., Choi, H., Kang, J., Yun, Y., Lett Doh, N.: Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping. Industrial Robot: An International Journal. 41(2), 203–212 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Archana Khurana.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khurana, A., Nagla, K.S. An Improved Method for Extrinsic Calibration of Tilting 2D LRF. J Intell Robot Syst 99, 693–712 (2020). https://doi.org/10.1007/s10846-020-01147-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-020-01147-7

Keywords

Navigation