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

  • Tiago BarrosEmail author
  • Luís Garrote
  • Ricardo Pereira
  • Cristiano Premebida
  • Urbano J. Nunes
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)


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.


LiDAR odometry Semi-supervised learning Incremental label propagation SLAM Pole-based localization 



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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tiago Barros
    • 1
    Email author
  • Luís Garrote
    • 1
  • Ricardo Pereira
    • 1
  • Cristiano Premebida
    • 1
    • 2
  • Urbano J. Nunes
    • 1
  1. 1.Institute of Systems and Robotics, Department of Electrical and Computer Engineering, Polo IIUniversity of CoimbraCoimbraPortugal
  2. 2.Department of Aeronautical and Automotive EngineeringLoughborough UniversityLoughboroughUK

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