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Indoor Vehicle Positioning Based on Multi-Sensor Data Fusion

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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.

indoor vehicle positioningmulti-sensor data fusionultra-widebandlinear Kalman filterextended Kalman filter

WANG Mingyang、SHI Liangren、LI Yuanlong

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Department of Automation,Shanghai Jiao Tong University

Key Laboratory of System Control and Information Processing of Ministry of Education,Shanghai 200240,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaShandong Key Research and Development Project

6190324961973215620220552019JZZY020131

2023

10.1007/s12204-023-2571-5

上海交通大学学报(英文版)
上海交通大学

上海交通大学学报(英文版)

影响因子：0.151
ISSN：1007-1172
年,卷(期)：2023.28(1)
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