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An Hybrid Registration Method for SLAM with the M8 Quanergy LiDAR

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Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

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Abstract

Simultaneous localization and mapping (SLAM) is process highly relevant for autonomous systems. Accurate sensing provided by range sensors such as the M8 Quanergy LiDAR improves the speed and accuracy of SLAM, which can become an integral part of the control of innovative autonomous cars. In this paper we propose a hybrid point cloud registration method that profits from the high accuracy of classic iterated closest points (ICP) algorithm, and the robustness of the Normal Distributions Transform (NDT) registration method. We report positive results in an in-house experiment encouraging further research and experimentation.

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Notes

  1. 1.

    https://doi.org/10.5281/zenodo.3636204.

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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.

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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
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  2. Manuel Graña
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Corresponding authors

Correspondence to Marina Aguilar-Moreno or Manuel Graña .

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Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Aguilar-Moreno, M., Graña, M. (2020). An Hybrid Registration Method for SLAM with the M8 Quanergy LiDAR. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-61705-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61704-2

  • Online ISBN: 978-3-030-61705-9

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