Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation
This addresses the problem of reliable navigation in GPS-denied environments for autonomous vehicles or urban mobility, but it is incremental as it builds on existing deep learning and inertial sensing methods.
The paper tackled the problem of estimating car speed using only low-cost inertial sensors for dead reckoning navigation, and the result was a deep neural network that improved position accuracy during a 4-minute drive without GNSS updates.
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.