论文标题
使用自适应学习方法进行数据融合的自动驾驶车辆
Autonomous Driving Vehicles Using Adaptive Learning Method for Data Fusion
论文作者
论文摘要
本文提出了一种自动驾驶车辆数据融合的自适应学习方法。该定位基于自适应卡尔曼滤波器（KF）中两个实时运动学（RTK）全局定位系统（GPS）单元的惯性测量单元（IMU）的整合。可观察性分析表明，$ i $）将单个GPS与IMU集成并不构成可观察到的系统； $ ii $）将两个GPS单元与IMU的集成在本地可观察到的系统中，前提是连接两个GPS天线的生产线与测量加速度的向量（即惯性和重力加速度之和）不平行。后一种情况使得由于陀螺漂移及其偏置而导致的估计方向上的误差是可能的，而无需其他工具进行绝对方向测量，例如磁性指南针。此外，为了应对GPS系统有时会丢失信号并接收不准确的位置数据的事实，自我调整过滤器估计与GPS测量噪声相关的协方差矩阵。这使KF只有在GPS收到的信息可靠地可用时，KF才能将GPS测量纳入数据融合过程中。
This paper presents an adaptive learning method for data fusion in autonomous driving vehicles. The localization is based on the integration of Inertial Measurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning System (GPS) units in an adaptive Kalman filter (KF). The observability analysis reveals that $i$) integration of a single GPS with IMU does not constitute an observable system; $ii$) integration of two GPS units with IMU results in a locally observable system provided that the line connecting two GPS antennas is not parallel with the vector of the measured acceleration, i.e., the sum of inertial and gravitational accelerations. The latter case makes it possible to compensate for the error in the estimated orientation due to gyro drift and its bias without needing additional instruments for absolute orientation measurements, e.g., a magnetic compass. Moreover, in order to cope with the fact that GPS systems sometimes lose their signal and receive inaccurate position data, the self-tuning filter estimates the covariance matrix associated with the GPS measurement noise. This allows the KF to incorporate GPS measurements in the data fusion process heavily only when the information received by GPS becomes reliably available.
