Evaluation of Range Sensing-based Place Recognition for Long-term Urban Localization

Weixin Ma, Huan Yin, Lei Yao, Yuxiang Sun, Zhongqing Su

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Place recognition is a critical capability for autonomous vehicles. It matches current sensor data with a pre-built database to provide coarse localization results. However, the effectiveness of long-term place recognition may be degraded by environment changes, such as seasonal or weather changes. To have a deep understanding of this issue, we conduct a comprehensive evaluation study on several state-of-the-art range sensing-based (i.e., LiDAR and radar) place recognition methods on the Borease dataset, which encapsulates long-term localization scenarios with stark seasonal variations and adverse weather conditions. In addition, we design a novel metric to evaluate the influence of matching thresholds on place recognition performance for long-term localization. Our results and findings provide fresh insights to the community and potential directions for future study.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Intelligent Vehicles
Publication statusAccepted/In press - 2024


  • Autonomous Vehicles
  • Feature extraction
  • Laser radar
  • Location awareness
  • Long-term Localization
  • Measurement
  • Meteorology
  • Place Recognition
  • Point cloud compression
  • Range Sensing
  • Sensors
  • Urban Environments

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Optimization
  • Artificial Intelligence


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