Evaluating the Limits of a LiDAR for an Autonomous Driving Localization

Lucas de Paula Veronese, Fernando Auat-Cheein*, Filipe Mutz, Thiago Oliveira-Santos, José E. Guivant, Edilson De Aguiar, Claudine Badue, Alberto Ferreira de Souza

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In general, proposed solutions for LiDAR-based localization used in autonomous cars require expensive sensors and computationally expensive mapping processes. Moreover, the global localization for autonomous driving is converging to the use of maps. Straightforward strategies to reduce the costs are to produce simpler sensors and use maps already available on the Internet. Here, an analysis is presented to show how simple can a LiDAR sensor be without degrading the localization accuracy that uses road and satellite maps together to globally pose the car. Three characteristics of the sensor are evaluated: the number of range readings, the amount of noise in the LiDAR readings, and the frame rate, with the aim of finding the minimum number of LiDAR lines, the maximum acceptable noise and the sensor frame rate needed to obtain an accurate position estimation. The analysis is performed using an autonomous car in complex field scenarios equipped with a 3D LiDAR Velodyne HDL-32E. Several experiments were conducted reducing the number of frames, the number of scans per 3D point-cloud and artificially adding up to 15% of error in the ray length. Among other results, we found that using only 4 vertical lines per scan and with an artificial error added up to 15% of the ray length, the car was capable to localize itself within 2.11 meters error average. All experimental results and the followed methodology are explained in detail herein.

Original languageEnglish
Pages (from-to)1449-1458
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number3
Early online date7 Apr 2020
Publication statusPublished - Mar 2021


  • Autonomous vehicle
  • localization
  • particle filter
  • sensor fusion

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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