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赵鹏程
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Personal Information
  • Supervisor of Master's Candidates
  • Name (Pinyin):Zhao Pengcheng
  • Date of Birth:1993-09-05
  • E-Mail:
  • Date of Employment:2019-12-07
  • Administrative Position:高级实验师
  • Education Level:With Certificate of Graduation for Doctorate Study
  • Business Address:武汉大学信息学部遥感信息工程学院（5号楼）315办公室
  • Gender:Male
  • Contact Information:+86 15972003670
  • Status:Employed
  • Alma Mater:武汉大学
  • Teacher College:School of Remote Sensing and Information Engineering
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Paper Publications
Current position: Home   >   Scientific Research   >   Paper Publications

A low-drift and real-time localisation and mapping method for handheld hemispherical view LiDAR-IMU integration system

  • Date of Publication:2025-01-07
  • Hits:

  • DOI number: 

    10.1111/phor.12447

  • Affiliation of Author(s): 

    School of Remote Sensing and Information Engineering, Wuhan University, China

  • Journal: 

    PHOTOGRAMMETRIC RECORD

  • Key Words: 

    Extended Kalman filters,Mapping,Optical radar,3D point cloud,Handhelds,Handhold LiDAR system,Hemispherical view LiDAR,Integration systems,LiDAR simultaneous localization and mapping,Localization method,Mapping method,Mobile measurements,Simultaneous localization and mapping,data set,experimental study,Kalman filter,lidar,mapping,mobile phone,segmentation,Iterative methods,3D point cloud,handheld LiDAR system,hemispherical view LiDAR,LiDAR SLAM,mobile measurement

  • Abstract: 

    This paper proposes a simultaneous localisation and mapping (SLAM) framework that uses a handheld hemispherical view LiDAR-IMU integration system. Inspired by the specific characteristic of the hemispherical view LiDAR, a ground segmentation module based on seed points is designed. The ground points are then downsampled to eliminate redundant vertical constraints. The IMU data and the pre-processed point cloud are used to perform state estimation via a tightly coupled iterative extended Kalman filter (iEKF) to obtain the pose estimation. The automatically detected loop closures provide closed-loop constraints for the odometry, and a factor graph ensures the global consistency of the map. Data from diverse scenes are collected via a prototype system. Both qualitative and quantitative experiments are carried out to verify the framework's performance. According to the experimental results, our framework achieves low-drift, high-coverage and real-time performance, outperforming the state-of-the-art LiDAR SLAM methods in our handheld hemispherical view LiDAR-IMU test sites. For the research community's benefit, the dataset is publicly provided for other researchers to compare against.

  • Co-author: 

    Hu,Duan, Pengcheng, Mingyao, Fei, Qingwu,Ai,Yu,Zhao, Xuzhe

  • Indexed by: 

    Journal paper

  • Discipline: 

    Engineering

  • Document Type: 

    J

  • Volume: 

    38

  • Issue: 

    182

  • Page Number: 

    176-196

  • ISSN No.: 

    0031-868X

  • Translation or Not: 

    no

  • CN No.: 

    Scopus:2-s2.0-85161370685,EI:20232414214472,WOS:001000654200001

  • Date of Publication: 

    2023-06-01

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