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

  1. Marina Aguilar-Moreno 1
  2. Manuel Graña 1
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Buch:
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020
  1. Álvaro Herrero (coord.)
  2. Carlos Cambra (coord.)
  3. Daniel Urda (coord.)
  4. Javier Sedano (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Verlag: Springer Suiza

ISBN: 978-3-030-57801-5,  978-3-030-57802-2

Datum der Publikation: 2021

Seiten: 824-834

Kongress: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)

Art: Konferenz-Beitrag

DIALNET GOOGLE SCHOLAR

Zusammenfassung

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.

Fuente de los datos: Dialnet