Vehicle Egomotion Estimation Through IMU-RADAR Tight-Coupling

Abstract

The current state-of-the-art vehicle egomotion state estimation systems are limited in their usage for advanced driver-assistance systems (ADAS), as the estimation accuracy is limited in driving scenarios where large amounts of wheel slip occur. Addressing the fundamental limitations of vehicle egomotion state estimation, this study investigates the usage of RADAR in vehicle egomotion state estimation through a tight-coupling between an Inertial Measurement Unit (IMU) and RADAR. The limitations of the state-of-the-art are caused by the usage of automotive grade sensors, which provide limited accuracy. RADAR is a type of sensor, which is already used extensively in ADAS, however, not yet in egomotion estimation. A reason for not using RADAR in this context is that it requires knowledge of the motion of the detected targets. In literature statistical methods are suggested to reject moving detections, but these are per definition not robust. This research, therefore, answers the question: How can RADAR be used in vehicle egomotion state estimation to improve performance and expand the capabilities of the system in a robust way? A new method is developed, which through IMU-RADAR tight-coupling, is able to reject moving detections. These stationary detections are then integrated into a Kalman filter to obtain the vehicle motion. This new method is compared to the state-of-the-art methods and the results are validated on a real data set of a vehicle driving in urban and highway setting. The findings demonstrate that the newly introduced method enhances the accuracy of vehicle egomotion state estimation.