Real-Time GICP: Direct LiDAR SLAM for CPU Environment

Kaiduo Fang, Ivan Wang Hei Ho

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


As a core part in modern Robotics research, Simultaneous Localization and Mapping (SLAM) is widely used in different kinds of autonomous robots, such as autonomous vehicles. Existing direct methods and feature-based methods in LiDAR-SLAM still have different limitations. In this paper, we propose a lightweight, robust and accurate LiDAR-SLAM framework named Real-Time GICP, which contains improved GICP method as front-end and Scan-Context loop detection to construct pose graph optimization. Experimental results show that Real-Time GICP can achieve state-of-the-art accuracy and superior computational efficiency comparing with other open-source GICP methods. Our method can achieve around 50 Hz for 64 rings Velodyne LiDAR with Intel i7 CPU, which makes Real-Time GICP more efficient in when deployed on popular robotics computational platforms.

Original languageEnglish
Title of host publicationProceedings of 2022 IEEE Region 10 International Conference, TENCON 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665450959
Publication statusPublished - Nov 2022
Event2022 IEEE Region 10 International Conference, TENCON 2022 - Virtual, Online, Hong Kong
Duration: 1 Nov 20224 Nov 2022

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2022 IEEE Region 10 International Conference, TENCON 2022
Country/TerritoryHong Kong
CityVirtual, Online


  • Autonomous Driving
  • Navigation
  • Robotics

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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