Indoor Vehicle Positioning Based on Multi-Sensor Data Fusion

doi:10.1007/s12204-023-2571-5

Abstract

Abstract: This study proposes a Kalman filter-based indoor vehicle positioning method for cases in which the steering angle and rotation speed of the vehicle’s wheels are unknown. By fusing the position and velocity data from the ultra-wideband sensors and acceleration and orientation data from the inertial measurement unit, we developed two algorithms to estimate the real-time position of the vehicle based on a linear Kalman filter and extended Kalman filter, respectively. We then conducted simulations and experiments to examine the performances of the algorithms. In the experiment, the Kalman filtering hyperparameters are configured, and we then ran the two algorithms to determine the positioning precision and accuracy with the ground truth produced via LiDAR. We verified that our method can improve precision and accuracy compared with the raw positioning data and can achieve desirable effects for indoor vehicle positioning when vehicles travel at low speeds.

Key words: indoor vehicle positioning, multi-sensor data fusion, ultra-wideband, linear Kalman filter, extended Kalman filter

摘要： 在车辆前轮转角和后轮线速度未知的前提下，本文提出了一种基于卡尔曼滤波的室内车辆定位方法。本文使用超宽带传感器获取室内车辆的位置和速度，用惯性测量单元获取车辆的加速度和朝向角。为融合这些信息提高实时定位性能，本文分别基于线性卡尔曼滤波和扩展卡尔曼滤波提出了两种算法。然后本文编写对应算法，通过仿真和实验验证方法的可行性并测试性能。在实验中，整定卡尔曼滤波的超参数后，运行两种算法并和LiDAR提供的真值做比对，计算各自的正确率和准确率。结果表明，在室内车辆低速运动时，本文的方法与传感器直接提供的定位数据相比可以有效提高正确率和准确率，取得理想的定位效果。

关键词: 室内车辆定位，多传感器数据融合，超宽带，线性卡尔曼滤波，扩展卡尔曼滤波

CLC Number:

TP391

WANG Mingyang (王明阳), SHI Liangren∗ (时良仁), LI Yuanlong (李元龙). Indoor Vehicle Positioning Based on Multi-Sensor Data Fusion[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 77-85.

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