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Iberian Robotics conference

ROBOT 2019: Robot 2019: Fourth Iberian Robotics Conference pp 255–266Cite as

Improving Localization by Learning Pole-Like Landmarks Using a Semi-supervised Approach

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1092))

Abstract

The aim of this paper is to contribute with an object-based learning and selection methods to improve localization and mapping techniques. The methods use 3D-LiDAR data which is suitable for autonomous driving systems operating in urban environments. The objects of interest to be served as landmarks are pole-like objects which are naturally present in the environment. To detect and recognize pole-like objects in 3D-LiDAR data, a semi-supervised iterative label propagation method has been developed. Additionally, a selection method is proposed for selection the best poles to be used in the localization loop. The LiDAR localization and mapping system is validated using data from the KITTI database. Reported results show that by considering the occurrence of pole-like objects over time leads to an improvement on both the learning model and the localization.

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Acknowledgements

This work was supported partially by the project MATIS (CENTRO-01-0145-FEDER-000014) co-financed by the European Regional Development Fund (FEDER) through of the Centro Regional Operacional Program (CENTRO2020), Portugal. It was also partially supported by the University of Coimbra, Institute of Systems and Robotics (ISR-UC) and FCT (Portuguese Science Foundation) through grant UID/EEA/00048/2019.

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Authors and Affiliations

  1. Institute of Systems and Robotics, Department of Electrical and Computer Engineering, Polo II, University of Coimbra, Coimbra, Portugal

    Tiago Barros, Luís Garrote, Ricardo Pereira, Cristiano Premebida & Urbano J. Nunes

  2. Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, UK

    Cristiano Premebida

Authors
  1. Tiago Barros
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  2. Luís Garrote
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  3. Ricardo Pereira
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  4. Cristiano Premebida
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  5. Urbano J. Nunes
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Corresponding author

Correspondence to Tiago Barros .

Editor information

Editors and Affiliations

  1. School of Engineering, Polytechnic Institute of Porto, Porto, Portugal

    Manuel F. Silva

  2. Department of Electrical Engineering, Polytechnic Institute of Bragança, Bragança, Portugal

    José Luís Lima

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    Luís Paulo Reis

  4. UPC, Universitat Politècnica de Catalunya, Barcelona, Spain

    Alberto Sanfeliu

  5. Centro Universitario de la Defensa (CUD), Zaragoza, Spain

    Danilo Tardioli

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Barros, T., Garrote, L., Pereira, R., Premebida, C., Nunes, U.J. (2020). Improving Localization by Learning Pole-Like Landmarks Using a Semi-supervised Approach. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-35990-4_21

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