论文标题
使用惯性传感器来学习汽车速度，以估算导航
Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation
论文作者
论文摘要
训练了深度神经网络（DNN），以估算在城市地区驾驶的汽车速度，并输入来自低成本六轴惯性测量单元（IMU）的测量流。通过在配备全球导航卫星系统（GNSS）实时运动学（RTK）定位设备和同步IMU的汽车中，通过驾驶以色列Ashdod市驾驶以色列市Ashdod市收集了三个小时的数据。使用以高速率获得的位置测量值计算了汽车速度的地面真相标签。提出了具有较长短期内存层的DNN体系结构，以实现高频速度估计，以说明以前的输入历史记录和速度，加速度和角速度之间的非线性关系。制定了一个简化的死者估算本地化方案，以评估训练有素的模型，该模型提供了速度伪测量。训练有素的模型显示在不使用GNSS位置更新的情况下，可以在4分钟内实质上提高位置准确性。
A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving through the city of Ashdod, Israel in a car equipped with a global navigation satellite system (GNSS) real time kinematic (RTK) positioning device and a synchronized IMU. Ground truth labels for the car speed were calculated using the position measurements obtained at the high rate of 50 Hz. A DNN architecture with long short-term memory layers is proposed to enable high-frequency speed estimation that accounts for previous inputs history and the nonlinear relation between speed, acceleration and angular velocity. A simplified aided dead reckoning localization scheme is formulated to assess the trained model which provides the speed pseudo-measurement. The trained model is shown to substantially improve the position accuracy during a 4 minutes drive without the use of GNSS position updates.
