3D Scene Reconstruction Based on a 2D Moving LiDAR

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 942)


A real-world reconstruction from a computer graphics tool is one of the main issues in two different communities: robotics and artificial intelligence, both of them under different points of view such as computer science, perception and machine vision. A real scene can be reconstructed by generating of point clouds with the help of depth sensors, rotational elements and mathematical transformations according to the mechanical design. This paper presents the development of a three-dimensional laser range finder based on a two-dimensional laser scanner Hokuyo URG-04LX-UG01 and a step motor. The design and kinematic model of the system to generate 3D point clouds are presented with an experimental acquisition algorithm implemented on Robotic Operative System ROS in Python language. The quality of the generated reconstruction is improved with a calibration algorithm based on a model parameter optimization from a reference surface, the results from the calibrated model were compared with a commercial low-cost device. The concurrent application of the system permits the viewing of the scene from different perspectives. The output files can be easily visualized with Python or MATLAB and used for surface reconstruction, scene classification or mapping. In this way, typical robotic tasks can be realized, highlighting autonomous navigation, collision avoidance, grasp calculation and handling of objects.


3D reconstruction Terrestrial LiDAR 3D point clouds Intrinsic calibration Machine vision 



This research is being developed with the partial support of the “Gobernación del Tolima” under “Proyecto Talento Humano” - Research Culture. The results presented in this paper have been obtained with the assistance of students from the Research Hotbed on Robotics (SI2C), Research Group D+TEC, Universidad de Ibagué, Ibagué-Colombia.


  1. 1.
    Levinson, J., et al.: Towards fully autonomous driving: systems and algorithms. In: 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 163–168. IEEE, June 2011Google Scholar
  2. 2.
    Reymann, C., Lacroix, S.: Improving LiDAR point cloud classification using intensities and multiple echoes. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5122–5128. IEEE, September 2015Google Scholar
  3. 3.
    Ocando, M.G., Certad, N., Alvarado, S., Terrones, A.: Autonomous 2D SLAM and 3D mapping of an environment using a single 2D LIDAR and ROS. In: 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), pp. 1–6. IEEE, November 2017Google Scholar
  4. 4.
    Wehr, A., Lohr, U.: Airborne laser scanning-an introduction and overview. ISPRS J. Photogrammetry Remote Sens. 54, 68–82 (1999)CrossRefGoogle Scholar
  5. 5.
    Morales, J., Martinez, J.L., Mandow, A., Pequeno-Boter, A., Garcia-Cerezo, A.: Design and development of a fast and precise low-cost 3D laser rangefinder. In: 2011 IEEE International Conference on Mechatronics, pp. 621–626. IEEE, April 2011Google Scholar
  6. 6.
    Klimentjew, D., Arli, M., Zhang, J.: 3D scene reconstruction based on a moving 2D laser range finder for service-robotsGoogle Scholar
  7. 7.
    Park, C.-S., Kim, D., You, B.-J., Oh, S.-R.: Characterization of the Hokuyo UBG-04LX-F01 2D laser rangefinder. In: 19th International Symposium in Robot and Human Interactive Communication, pp. 385–390. IEEE, September 2010Google Scholar
  8. 8.
    Guo, C.X., Roumeliotis, S.I.: An analytical least-squares solution to the line scan LIDAR-camera extrinsic calibration problem. In: 2013 IEEE International Conference on Robotics and Automation, pp. 2943–2948. IEEE, May 2013Google Scholar
  9. 9.
    Martínez, J.L., Morales, J., Reina, A.J., Mandow, A., Pequeño-Boter, A., García-Cerezo, A.: Construction and calibration of a low-cost 3D laser scanner with 360\(^\circ \) field of view for mobile robotsGoogle Scholar
  10. 10.
    Morales, J., Martínez, J., Mandow, A., Reina, A., 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, 20025–20040 (2014)CrossRefGoogle Scholar
  11. 11.
    Zeng, Y., et al.: An improved calibration method for a rotating 2D LIDAR system. Sensors 18, 497 (2018)CrossRefGoogle Scholar
  12. 12.
    Mader, D., Westfeld, P., Maas, H.-G.: An Integrated flexible self-calibration approach for 2D laser scanning range finders applied to the Hokuyo UTM-30LX-EWGoogle Scholar
  13. 13.
    Olivka, P., Krumnikl, M., Moravec, P., Seidl, D.: Calibration of short range 2D laser range finder for 3D SLAM usage. J. Sens. 2016, 1–13 (2016)CrossRefGoogle Scholar
  14. 14.
    Kang, J., Doh, N.L.: Full-DOF calibration of a rotating 2-D LIDAR with a simple plane measurement. IEEE Trans. Robot. 32, 1245–1263 (2016)CrossRefGoogle Scholar
  15. 15.
    Mallet, C., Bretar, F.: Full-waveform topographic lidar: state-of-the-art. ISPRS J. Photogramm. Remote Sens. 64, 1–16 (2009)CrossRefGoogle Scholar
  16. 16.
    Okubo, Y., Okubo, Y., Ye, C., Borenstein, J.: Characterization of the Hokuyo URG-04LX laser rangefinder for mobile robot obstacle negotiation. Spie Def., sec.+sens.; Unmanned Sys. Tech. XI, Conf. 7332: Unmanned, Robotic, and Layered Systems (2009)Google Scholar
  17. 17.
    Muhammad, N., Lacroix, S.: Calibration of a rotating multi-beam Lidar. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5648–5653. IEEE, October 2010Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Facultad de Ingeniería, Grupo de Investigación D+TEC, Programa de Ingeniería ElectrónicaUniversidad de IbaguéIbaguéColombia

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