A low-drift and real-time localisation and mapping method for handheld hemispherical view LiDAR-IMU integration system
发布时间：2025-01-07 点击次数：
DOI码：10.1111/phor.12447
所属单位：School of Remote Sensing and Information Engineering, Wuhan University, China
发表刊物：PHOTOGRAMMETRIC RECORD
关键字：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
摘要：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.
合写作者：Hu,Duan, Pengcheng, Mingyao, Fei, Qingwu,Ai,Yu,Zhao, Xuzhe
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：38
期号：182
页面范围：176-196
ISSN号：0031-868X
是否译文：否
CN号：Scopus:2-s2.0-85161370685,EI:20232414214472,WOS:001000654200001
发表时间：2023-06-01
