PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization

Wei Wang, Bing Wang, Peijun Zhao, Changhao Chen, Ronald Clark, Bo Yang, Andrew Markham, Niki Trigoni

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


In this paper, we present a novel end-to-end learning-based LiDAR sensor relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input. Compared to visual sensor-based relocalization, LiDAR sensors can provide rich and robust geometric information about a scene. However, point clouds of LiDAR sensors are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360° LiDAR sensor frames. Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposed method can achieve accurate relocalization performance.

Original languageEnglish
Pages (from-to)959-968
Number of pages10
JournalIEEE Sensors Journal
Issue number1
Publication statusPublished - 1 Jan 2022
Externally publishedYes


  • LiDAR point cloud
  • LiDAR sensor relocalization
  • sensor applications

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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