一种LiDAR和PDR融合的室内目标定位方法
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1.桂林理工大学;2.广西壮族自治区自然资源信息中心;3.广西产研院人工智能与大数据应用研究所

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TN92

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国家自然科学基金项目(面上项目,重点项目,重大项目)


An indoor target localization method with LiDAR and PDR fusion
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1.Guilin University of Technology;2.Guangxi Natural Resources Information Center;3.Institute of Artificial Intelligence and Big Data Application,Guangxi Industry Research Institute

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    摘要:

    激光雷达(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。本文提出一种LiDAR和PDR融合的室内定位方法,以扩展卡尔曼滤波(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均有效提高,PDR定位误差为0.98m,LiDAR定位误差为0.6m,EKF滤波融合后定位误差可以下降到0.32m。

    Abstract:

    LiDAR has the advantages of strong anti-interference capability and high speed, angle and distance resolution in indoor positioning, but its accuracy is easily affected by the interference of environmental factors during the positioning process. In this paper, we propose an indoor positioning method for LiDAR and PDR fusion, based on the Extended Kalman Filter (EKF), by solving the displacement increments and angular observations of LiDAR and the positional information of PDR, so that the two can be complementarily fused. The problems of non-line-of-sight influence and error accumulation are effectively suppressed, and the positioning accuracy of single-class combination algorithm and fusion-class combination algorithm are compared and analyzed. The experimental results show that the LiDAR and PDR fusion localization algorithm is effective in improving the accuracy and stability compared with the single localization method when the indoor personnel are walking. The localization error is 0.98m for PDR and 0.6m for LiDAR, and the localization error can be reduced to 0.32m after EKF filtering and fusion.

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引用本文

李景文,韦晶闪,陆妍玲,等. 一种LiDAR和PDR融合的室内目标定位方法[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-09-28
  • 最后修改日期:2022-05-01
  • 录用日期:2022-05-05
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