Evaluation of Long-term LiDAR Place Recognition

Jukka Peltomäki, Farid Alijani, Jussi Puura, Heikki Huttunen, Esa Rahtu, J. -K. Kämäräinen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

We compare a state-of-the-art deep image retrieval and a deep place recognition method for place recognition using LiDAR data. Place recognition aims to detect previously visited locations and thus provides an important tool for navigation, mapping, and localisation. Experimental comparisons are conducted using challenging outdoor and indoor datasets, Oxford Radar RobotCar and COLD, in the "long-term" setting where the test conditions differ substantially from the training and gallery data. Based on our results the image retrieval methods using LiDAR depth images can achieve accurate localization (the single best match recall 80%) within 5.00 m in urban outdoors. In office indoors the comparable accuracy is 50 cm but is more sensitive to changes in the environment.
Original languageEnglish
Title of host publication2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages4487-4492
Number of pages6
ISBN (Electronic)978-1-6654-1714-3
DOIs
Publication statusPublished - 2021
Publication typeA4 Article in conference proceedings
EventIEEE/RSJ International Conference on Intelligent Robots and Systems -
Duration: 27 Sept 20211 Oct 2021

Publication series

NameProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Period27/09/211/10/21

Keywords

  • Training
  • Meters
  • Location awareness
  • Laser radar
  • Image recognition
  • Image retrieval
  • Radar imaging

Publication forum classification

  • Publication forum level 1

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