Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation
Maxim Freydin, Barak Or
Abstract
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 were collected by driving through the city of Ashdod, Israel, in a car equipped with a global navigation satellite system (GNSS) real-time kinematic 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 input 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 pseudomeasurement. The trained model is shown to substantially improve the position accuracy during a 4-min drive without the use of GNSS position updates.