摘要
激光雷达(light detection and ranging,LiDAR)在室内定位中具有抗干扰能力强,速度、角和距离分辨率高等优点,但在定位过程中其精度易受环境因素干扰影响。提出了一种LiDAR和行人航迹推算(pedestrian dead reckoning,PDR)融合的室内定位方法,以扩展卡尔曼滤波(extended Kalman filter,EKF)为基础,通过对LiDAR的位移增量、角度观测值以及PDR的位姿信息等量测值进行解算,令二者互补融合,有效抑制非视距影响和误差累积的问题,并对单一类组合算法和融合类组合算法的定位精度进行对比分析。实验结果表明:当室内人员为行走状态时,LiDAR和PDR融合定位算法较单一定位方法在精度和稳定性方面均有效提高,PDR定位误差为0.98 m,LiDAR定位误差为0.6 m,EKF融合后定位误差下降到0.32 m。
Although light detection and ranging(LiDAR)has the advantages of high anti-interference capability and high speed,angle and distance resolution in indoor positioning,its positioning accuracy is easily affected by interference from environmental factors.A LiDAR and PDR fusion method for indoor positioning was proposed,based on the extended Kalman filter(EKF),by solving the quantitative measurements such as displacement increments and angular observations of LiDAR and positional information of pedestrian dead reckoning(PDR),the problem of non-line-of-sight influence and error accumulation was effectively suppressed through the complementary fusion of the two,and the localization accuracy of the single class combined algorithm and the fused class combined algorithm was compared and analyzed.From the experimental results,it can be concluded that when the indoor personnel are walking,LiDAR and PDR fusion localization algorithm can effectively improve the accuracy and stability compared with the single localization method.The localization error within 0.98 m for PDR and 0.6 m for LiDAR,and the localization error can be reduced to 0.32 m after the EKF filtering fusion.
作者
李景文
韦晶闪
陆妍玲
姜建武
朱明
叶波
张英南
LI Jing-wen;WEI Jing-shan;LU Yan-ling;JIANG Jian-wu;ZHU Ming;YE Bo;ZHANG Ying-nan(College of Geomatics and Geoinfornation Guilin University of Technology,Guilin 541004,China;Guangxi Key Laboratory of Spatial Information and Geomatics,Guilin 541004,China;Guangxi Natural Resources Information Center,Nanning 510023,China;Institute of Artificial Intelligence and Big Data Application,Guangxi Industry Research Institute,Nanning 530200,China)
出处
《科学技术与工程》
北大核心
2022年第25期11068-11074,共7页
Science Technology and Engineering
基金
国家自然科学基金(41961063)
广西自然科学基金创新研究团队项目(2019GXNSFGA245001)
国家文化和旅游科技创新工程项目(2019-011)。
