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Comparison of Major LiDAR Data-Driven Feature Extraction Methods for Autonomous Vehicles

  • Duarte Fernandes
  • Rafael Névoa
  • António SilvaEmail author
  • Cláudia Simões
  • João Monteiro
  • Paulo Novais
  • Pedro Melo
Conference paper
  • 308 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1160)

Abstract

Object detection is one of the areas of computer vision that has matured very rapidly. Nowadays, developments in this research area have been playing special attention to the detection of objects in point clouds due to the emerging of high-resolution LiDAR sensors. However, data from a Light Detection and Ranging (LiDAR) sensor is not characterised by having consistency in relative pixel densities and introduces a third dimension, raising a set of drawbacks. The following paper presents a study on the requirements of 3D object detection for autonomous vehicles; presents an overview of the 3D object detection pipeline that generalises the operation principle of models based on point clouds; and categorises the recent works on methods to extract features and summarise their performance.

Keywords

LiDAR Point clouds 3D Object Detection and Classification CNNs 

Notes

Acknowledgements

This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n\(^{\circ }\) 037902; Funding Reference: POCI-01-0247-FEDER-037902]

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Duarte Fernandes
    • 1
  • Rafael Névoa
    • 1
  • António Silva
    • 1
    Email author
  • Cláudia Simões
    • 2
  • João Monteiro
    • 1
  • Paulo Novais
    • 1
  • Pedro Melo
    • 1
    • 3
  1. 1.Algoritmi CentreUniversity of MinhoBragaPortugal
  2. 2.BoschBragaPortugal
  3. 3.Universidade de Trás-os-Montes e Alto DouroVila RealPortugal

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