基于LiDAR点云的UWB NLOS识别及室内融合定位算法  

Recognition of UWB NLOS in LiDAR Point Clouds and Indoor Fusion Positioning Algorithm

作　　者：隋心[1,3] 马昊楠 王长强[1] 陈志键 史政旭 高佳鑫 SUI Xin;MA Haonan;WANG Changqiang;CHEN Zhijian;SHI Zhengxu;GAO Jiaxin(School of Geomatics and Geographic Science,Liaoning Technical University,Fuxin 123000,China;Department of Military Oceanography and Surveying,Dalian Naval Academy,Dalian 116018,China;Erdos Research Institute,Liaoning Technical University,Erdos 017004,China)

机构地区：[1]辽宁工程技术大学测绘与地理科学学院,阜新123000 [2]大连舰艇学院军事海洋与测绘系,大连116018 [3]辽宁工程技术大学鄂尔多斯研究院,鄂尔多斯017000

出　　处：《地球信息科学学报》2024年第12期2686-2700,共15页Journal of Geo-information Science

基　　金：国家自然科学基金项目(42404045);辽宁省自然科学基金计划博士科研启动项目(2024-BS-256);2024年度辽宁省教育厅基本科研项目(LJ212410147093);辽宁工程技术大学鄂尔多斯研究院校地科技合作培育项目(YJY-XD-2024-B-007);辽宁省重点研发计划项目(2020JH2/10100044);辽宁省“兴辽英才计划”项目(XLYC2002101、XLYC2008034)。

摘　　要：针对室内环境中超宽带(Ultra-Wideband,UWB)信号易受障碍物遮挡导致非视距(Non Line of Sight,NLOS)误差的问题,本文提出了一种基于激光雷达(Light Detection And Ranging,LiDAR)点云识别UWB NLOS的融合定位方法,该方法利用LiDAR点云信息辅助UWBNLOS识别,并通过UWB视距(LineofSight,LOS)测距值消除LiDAR同时定位与建图(Simultaneous Localization and Mapping,SLAM)过程中的累计误差,从而提高室内融合定位精度和鲁棒性。首先,采用八叉树对LiDAR点云进行处理,根据UWB基准站位置信息构建测距方向,并从LiDAR点云中提取测距方向上相关区域的点云数据。然后,通过3D Alpha Shape算法对所提取点云中可能阻碍UWB信号传播的障碍物进行轮廓提取。此外,根据分析提取的障碍物轮廓和UWB测距方向的空间关系,以此有效判定UWB信号是否存在NLOS测距情况。最后,剔除UWB测距过程中存在的NLOS测距值,通过紧组合方式,采用扩展卡尔曼滤波(EKF)将UWB LOS测距值和LiDAR SLAM的定位信息进行融合解算,消除LiDAR SLAM定位结果中的累积误差,以此提高融合定位精度和鲁棒性。为验证本文所提出的融合定位算法的有效性,通过搭建的融合定位实验平台在教学楼大厅进行了NLOS静态识别实验,在地下停车场进行了动态NLOS识别与动态定位实验。实验结果表明,该方法能够显著提高在室内复杂环境中的NLOS识别与定位的准确性,相较于单传感器定位与UWB原始测距值与LiDAR SLAM紧组合EKF的定位方法,NLOS识别准确率为93.22%,定位精度分别提高了49.24%、47.03%、96.13%,定位误差为0.067 m,实现了亚分米级室内定位。Addressing the issue of Ultra-Wideband(UWB)signal obstruction by obstacles in indoor environments,which leads to Non Line of Sight(NLOS)errors,this paper presents a fusion positioning method based on Light Detection And Ranging(LiDAR)point cloud for identifying UWB NLOS.This method utilizes LiDAR point cloud information to assist in the identification of UWB NLOS and leverages UWB Line of Sight(LOS)ranging values to eliminate cumulative errors in the LiDAR Simultaneous Localization and Mapping(SLAM)positioning process,thereby enhancing the accuracy and robustness of indoor fusion positioning.Initially,the method processes the LiDAR point cloud using an octree,constructs the ranging direction based on the location information of UWB base stations,and extracts the relevant point cloud data in the ranging direction from the LiDAR point cloud.Subsequently,the 3D Alpha Shape algorithm is employed to extract contours of obstacles that may hinder UWB signal propagation within the extracted point clouds.Furthermore,by analyzing the spatial relationship between the extracted obstacle contours and the UWB ranging direction,the presence of NLOS conditions in UWB signals is effectively determined.Finally,NLOS ranging values identified during the UWB ranging process are excluded,and a tight integration approach is used with an Extended Kalman Filter(EKF)to fuse UWB LOS ranging values with LiDAR SLAM positioning data,eliminating cumulative errors in the LiDAR SLAM positioning outcomes,thereby enhancing the precision and robustness of fusion positioning.Experimental results demonstrate that this method significantly improves positioning accuracy in indoor environments,increasing the positioning accuracy by 96.13%compared to the positioning method that tightly combines the original UWB ranging values with LiDAR SLAM using EKF,with a positioning error of 0.067 m,achieving sub-meter level indoor positioning accuracy.

关 键 词：LiDAR SLAM 3D Alpha Shape 点云轮廓提取 超宽带非视距识别 室内融合定位 

分 类 号：TN958.98[电子电信—信号与信息处理] TN925[电子电信—信息与通信工程]