Season-Invariant and Viewpoint-Tolerant LiDAR Place Recognition in GPS-Denied Environments

Fengkui Cao, Fei Yan, Sen Wang, Yan Zhuang, Wei Wang

Research output: Contribution to journalArticle

Abstract

Place recognition remains a challenging problem under various perceptual conditions, e.g., all weather, times of day, seasons, and viewpoint shifts. Different from most of the existing place recognition methods using pure vision, this article studies light detection and ranging (LiDAR) based approaches. Point clouds have some benefits for place recognition since they do not suffer from illumination changes. On the other hand, they are dramatically affected by structural changes from different viewpoints or across seasons. In this article, a novel LiDAR-based place recognition system is proposed to achieve long-term robust localization, even under severe seasonal changes and viewpoint shifts. To improve the efficiency, a compact cylindrical image model is designed to convert three-dimensional point clouds to two-dimensional images representing the prominent geometric relationships of scenes. The contexts (buildings, trees, road structures, etc.) of scenes are utilized for efficient place recognition. A sequence-based temporal consistency check is also introduced for postverification. Extensive real experiments on three datasets (Oxford RobotCar [1] , NCLT [2] , and DUT-AS) show that the proposed system outperforms both state-of-the-art visual and LiDAR-based methods, verifying its robust performance in challenging scenarios.
Original languageEnglish
Pages (from-to)563-574
Number of pages12
JournalIEEE Transactions on Industrial Electronics
Volume68
Issue number1
Early online date1 Jan 2020
DOIs
Publication statusE-pub ahead of print - 1 Jan 2020

Keywords

  • Across season
  • light detection and ranging (LiDAR) sensors
  • long-term localization
  • mobile robots
  • place recognition

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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