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Asynchronous Sensor Fusion of GPS, IMU and CAN-Based Odometry for Heavy-Duty Vehicles
IEEE Transactions on Vehicular Technology ( IF 6.8 ) Pub Date : 2021-08-04 , DOI: 10.1109/tvt.2021.3101515
Vicent Girbes-Juan , Leopoldo Armesto , Daniel Hernandez-Ferrandiz , Juan Francisco Dols , Antonio Sala

In heavy-duty vehicles, multiple signals are available to estimate the vehicle's kinematics, such as Inertial Measurement Unit (IMU), Global Positioning System (GPS) and linear and angular speed readings from wheel tachometers on the internal Controller Area Network (CAN). These signals have different noise variance, bandwidth and sampling rate (being the latter, possibly, irregular). In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing. It is applied to achieve accuracy improvements when incorporating odometry measurements from CAN bus to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that this asynchronous multi-sensor (GPS+IMU+CAN-based odometry) fusion is advantageous in low-speed manoeuvres, improving accuracy and robustness to missing data, thanks to non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother, with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants; it can provide data to subsequent processing steps at arbitrary time instants, not necessarily coincident with the original measurement ones. Given the extra information available in the smoothing case, its estimation performance is less sensitive to the noise-variance parameter setting, compared to causal filtering. Working Matlab code is provided at the end of this work.

中文翻译:

用于重型车辆的 GPS、IMU 和基于 CAN 的里程计的异步传感器融合

在重型车辆中,多个信号可用于估计车辆的运动学,例如惯性测量单元 (IMU)、全球定位系统 (GPS) 以及来自内部控制器局域网 (CAN) 上车轮转速计的线性和角速度读数。这些信号具有不同的噪声方差、带宽和采样率(后者可能是不规则的)。在本文中,我们提出了一种非线性传感器融合算法,允许异步采样和非因果平滑。它用于在将来自 CAN 总线的里程计测量与标准 GPS+IMU 运动学估计相结合时实现精度改进,以及对丢失数据的鲁棒性。我们的结果表明,这种异步多传感器(基于 GPS+IMU+CAN 的里程计)融合在低速机动中具有优势,由于非因果过滤,提高了对缺失数据的准确性和鲁棒性。所提出的算法基于扩展卡尔曼滤波器和平滑器,对连续时间随机微分方程进行指数离散化,以处理任意时刻的测量值;它可以在任意时刻为后续处理步骤提供数据,不一定与原始测量一致。鉴于平滑情况下可用的额外信息,与因果过滤相比,其估计性能对噪声方差参数设置不太敏感。工作结束时提供了 Matlab 代码。对连续时间随机微分方程进行指数离散化,以便处理任意时刻的测量;它可以在任意时刻为后续处理步骤提供数据,不一定与原始测量一致。鉴于平滑情况下可用的额外信息,与因果过滤相比,其估计性能对噪声方差参数设置不太敏感。工作结束时提供了 Matlab 代码。对连续时间随机微分方程进行指数离散化,以便处理任意时刻的测量;它可以在任意时刻为后续处理步骤提供数据,不一定与原始测量一致。鉴于平滑情况下可用的额外信息,与因果过滤相比,其估计性能对噪声方差参数设置不太敏感。工作结束时提供了 Matlab 代码。与因果过滤相比,它的估计性能对噪声方差参数设置不太敏感。工作结束时提供了 Matlab 代码。与因果过滤相比,它的估计性能对噪声方差参数设置不太敏感。工作结束时提供了 Matlab 代码。
更新日期:2021-09-21
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