Skip to main content
SpringerLink
Log in

A Comparison of Registration Methods for SLAM with the M8 Quanergy LiDAR

  • Conference paper
  • First Online:
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) (SOCO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

Abstract

LiDAR based SLAM is becoming affordable by new sensors such as the M8 Quanergy LiDAR, but there is still little work reporting on the accuracy attained with them. In this paper we report on the comparison of three registration methods applied to the estimation of the path followed by the LiDAR sensor and the registration of the overall cloud of points, namely the iterated closest points (ICP), Coherent Point Drift (CPD), and Normal Distributions Transform (NDT) registration methods. In our experiment, we found that the NDT method provides the most robust performance.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://doi.org/10.5281/zenodo.3633727.

References

  1. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  2. Biber, P., Straßer, W.: The normal distributions transform: a new approach to laser scan matching, vol. 3, pp. 2743–2748, November 2003

    Google Scholar 

  3. Caminal, I., Casas, J.R., Royo, S.: SLAM-based 3D outdoor reconstructions from LIDAR data. In: 2018 International Conference on 3D Immersion (IC3D), pp. 1–8, December 2018

    Google Scholar 

  4. Deng, Y., Shan, Y., Gong, Z., Chen, L.: Large-scale navigation method for autonomous mobile robot based on fusion of GPS and lidar SLAM. In: 2018 Chinese Automation Congress (CAC), pp. 3145–3148, November 2018

    Google Scholar 

  5. Levinson, J., Thrun, S.: Robust vehicle localization in urban environments using probabilistic maps. In: 2010 IEEE International Conference on Robotics and Automation, pp. 4372–4378, May 2010

    Google Scholar 

  6. Levinson, J., Thrun, S.: Unsupervised Calibration for Multi-beam Lasers, pp. 179–193. Springer, Heidelberg (2014)

    Google Scholar 

  7. Lu, J., Wang, W., Fan, Z., Bi, S., Guo, C.: Point cloud registration based on CPD algorithm. In: 2018 37th Chinese Control Conference (CCC), pp. 8235–8240, July 2018

    Google Scholar 

  8. Magnusson, M., Lilienthal, A., Duckett, T.: Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robot. 24, 803–827 (2007)

    Article  Google Scholar 

  9. Mitteta, M.A., Nouira, H., Roynard, X., Goulette, F., Deschaud, J.E.: Experimental assessment of the quanergy M8 LIDAR sensor. In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 41B5, pp. 527–531, June 2016

    Google Scholar 

  10. Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2262–2275 (2010)

    Article  Google Scholar 

  11. Pomerleau, F., Colas, F., Siegwart, R., Magnenat, S.: Comparing ICP variants on real-world data sets. Autonom. Robots 34(3), 133–148 (2013)

    Article  Google Scholar 

  12. Wu, D., Meng, Y., Zhan, K., Ma, F.: A LIDAR slam based on point-line features for underground mining vehicle. In: 2018 Chinese Automation Congress (CAC), pp. 2879–2883, November 2018

    Google Scholar 

  13. Xuexi, Z., Guokun, L., Genping, F., Dongliang, X., Shiliu, L.: Slam algorithm analysis of mobile robot based on LIDAR. In: 2019 Chinese Control Conference (CCC), pp. 4739–4745, July 2019

    Google Scholar 

  14. Yagfarov, R., Ivanou, M., Afanasyev, I.: Map comparison of LIDAR-based 2D slam algorithms using precise ground truth. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1979–1983, November 2018

    Google Scholar 

Download references

Acknowledgments

This work has been partially supported by FEDER funds through MINECO project TIN2017-85827-P, and grant IT1284-19 as university research group of excellence from the Basque Government.

Author information

Authors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Marina Aguilar-Moreno & Manuel Graña

Authors
  1. Marina Aguilar-Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Graña
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Marina Aguilar-Moreno or Manuel Graña .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aguilar-Moreno, M., Graña, M. (2021). A Comparison of Registration Methods for SLAM with the M8 Quanergy LiDAR. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_79

Download citation

Publish with us

Policies and ethics

Search

Navigation