Re-emission and satellite aerial maps applied to vehicle localization on urban environments

Lucas de Paula Veronese, Edilson De Aguiar, Rafael Correia Nascimento, José Guivant, Fernando A. Auat Cheein, Alberto Ferreira de Souza, Thiago Oliveira-Santos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Citations (Scopus)

Abstract

Vehicle localization in large-scale urban environments has been commonly addressed as a map-matching problem in the literature. Generally, the maps are 2D images of the world where each pixel covers a part of it. However, building maps for large-scale urban environments requires driving the vehicle along the desired path at least once. In order to simplify this task, in this work, we propose a new localization system that uses satellite aerial map-images available on the Internet to localize a vehicle in a complex urban environment. Satellite aerial map-images are compared against re-emission maps built from the infrared reflectance information of the vehicle's LiDAR. Normalized Mutual Information (NMI) is used to compare re-emission and aerial map images. A Particle Filter Localization strategy is applied for vehicle's localization. As a result, the system has an accuracy of 0.89m in a test course with 6.5km. Our system can be used continuously without losing track, and it works even in dark and partially occluded areas.

Original languageEnglish
Title of host publication2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages4285-4290
Number of pages6
ISBN (Electronic)9781479999941
DOIs
Publication statusPublished - 17 Dec 2015
Event28th IEEE/RSJ International Conference on Intelligent Robots and Systems 2015 - Hamburg, Germany
Duration: 28 Sept 20152 Oct 2015

Conference

Conference28th IEEE/RSJ International Conference on Intelligent Robots and Systems 2015
Abbreviated titleIROS 2015
Country/TerritoryGermany
CityHamburg
Period28/09/152/10/15

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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