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.
Similar content being viewed by others
References
Ye, C.: Navigating a mobile robot by a traversability field histogram. IEEE Trans. Syst. Man Cybern. B Cybern. 37(2), 361–372 (2007)
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)
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)
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)
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
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)
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)
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)
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)
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)
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)
User’s Manual and Programming Guide HDL-64E S, Velodyne, Morgan Hill, CA, USA (2013). www.velodynelidar.com
RobotEye RE08 3D-LiDAR 3D Laser Scanning System Product Datasheet, Ocular Robotics, Kingsgrove, Australia (2015). www.ocularrobotics.com
Khurana, A., Nagla, K.S.: Signal averaging for noise reduction in Mobile robot 3D measurement system. MAPAN. 33(1), 33–41 (2018)
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)
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)
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)
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)
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)
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
Sheehan, M., Harrison, A., Newman, P.: Self-calibration for a 3D laser. Int. J. Robot. Res. 31(5), 675–687 (2012)
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)
Alismail, H., Browning, B.: Automatic calibration of spinning actuated lidar internal parameters. J. Field Rob. 32(5), 723–747 (2015)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Zhao, R., Fan, J.: Global complexity bound of the Levenberg–Marquardt method. Optim. Methods and Softw. 31(4), 805–814 (2016)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-020-01147-7