首页|基于改进天牛须优化粒子滤波的UWB/LiDAR室内定位方法

基于改进天牛须优化粒子滤波的UWB/LiDAR室内定位方法

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针对超宽带(UWB)测距存在非视距(NLOS)误差以及LiDAR存在累计误差影响定位精确度的问题，提出一种基于改进天牛须搜索算法(IBAS)优化粒子滤波的UWB/LiDAR室内定位方法，该方法综合UWB抗干扰能力强、时间分辨率高和LiDAR高精度、高效率的优点，使用LiDAR量测信息解算组合定位系统与UWB基站的距离，剔除UWB量测值中的NLOS误差。改进天牛须搜索算法的引入可消除粒子滤波的粒子退化现象，减少算法所需粒子数，提升算法运行速度和实时性。最后构建UWB/LiDAR的组合函数，使用图优化方法优化全局位姿。实验结果表明，同等效果下经改进天牛须搜索算法优化后所需的粒子数仅为原粒子滤波算法的20％，同时相较于单一的UWB、LiDAR定位，所提出方法的定位精度分别提升了 44。75％和74。47％，效果良好。
UWB/LiDAR indoor positioning method based on improved beetle antennae search algorithm optimized particle filter
This paper proposes a UWB/LiDAR indoor positioning method based on improved beetle antennae search algorithm(IBAS)optimized particle filtering to address the issues of non-line-of-sight(NLOS)errorss in ultra-wideband(UWB)range and cumulative errors in LiDAR impacting positioning accuracy.In order to eliminate NLOS errors in the UWB measurement value,the method combines the benefits of high interference immunity and high temporal resolution of UWB with the high accuracy and efficiency of LDIAR.The distance between the combined positioning system and the UWB base station is solved using the LiDAR measurement information.The addition of the IBAS significantly reduces the number of particles needed by the algorithm,speeds up algorithm execution in real time,and successfully slows down the particle degradation issue.The global bit posture is then obtained by the graph optimization-based combination function of UWB/LiDAR.The experimental results show that the IBAS optimization algorithm only requires 20％particles of the original particle filtering algorithm for the same effect,and that when compared to single UWB and LiDAR localization,the localization accuracy of this proposed method is improved by 44.75％and 74.47％,respectively,with good results.

indoor positioningultra-widebandlidarparticle filteringimproved beetle antennae search algorithm

黄家才、王徐寅、高芳征、薛源

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南京工程学院机械工程学院,南京 211167

江苏省仿生控制技术与装备工程研究中心,南京 211167

南京工程学院自动化学院,南京 211167

室内定位 超宽带 激光雷达 粒子滤波 改进天牛须搜索算法

国家自然科学基金面上项目江苏省重点研发计划课题江苏省自然科学基金面上项目江苏省研究生实践创新计划项目江苏省青蓝工程人才培养项目

61873120BE2021016-5BK20201469SJCX22_1056

2024

10.13195/j.kzyjc.2023.0823

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子：1.227
ISSN：1001-0920
年,卷(期)：2024.39(10)