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An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

机译：基于一组预测模型和网格约束的无人驾驶汽车精确GPS-IMU / DR数据融合方法

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A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.
机译：具有实时运动学的高性能差分全球定位系统（GPS）接收器可为无人驾驶汽车提供绝对定位。然而，它不仅容易受到多径效应的影响，而且还不能有效地在广泛的驱动区域中实现精确的纠错。本文基于一组预测模型和占用栅格约束，提出了一种精确的GPS惯性测量单元（IMU）/航位推算（DR）数据融合方法。首先，我们使用一组具有不同结构参数的自回归和移动平均（ARMA）方程来构建原始导航的最大似然模型。其次，需要对所有预测结果和当前测量值进行网格约束和空间共识检查，以消除异常值。满足静态随机过程的导航数据将进一步融合以实现准确的定位结果。第三，多模式数据融合的标准偏差可以通过网格大小预先指定。最后，我们对各种真实的城市场景进行了大量的现场测试。实验结果表明，该方法可以显着平滑偏置中的小跳变，并大大减少了DR引起的累积位置误差。由于计算复杂度低，我们的方法在同一数据集上的定位精度超过了现有技术水平，并且新的数据融合方法已在我们的无人驾驶汽车中得到实际应用。

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