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.
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]. Journal of Shanghai Jiaotong University(Science), 2023
, 28(1)
: 77
-85
.
DOI: 10.1007/s12204-023-2571-5
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