Instantaneous ego-motion estimation using multiple Doppler radars

The estimation of the ego-vehicle's motion is a key capability for advanced driving assistant systems and mobile robot localization. The following paper presents a robust algorithm using radar sensors to instantly determine the complete 2D motion state of the ego-vehicle (longitudinal, lateral velocity and yaw rate). It evaluates the relative motion between at least two Doppler radar sensors and their received stationary reflections (targets). Based on the distribution of their radial velocities across the azimuth angle, non-stationary targets and clutter are excluded. The ego-motion and its corresponding covariance matrix are estimated. The algorithm does not require any preprocessing steps such as clustering or clutter suppression and does not contain any model assumptions. The sensors can be mounted at any position on the vehicle. A common field of view is not required, avoiding target association in space. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.

Authors

• Dominik Kellner
• Michael Barjenbruch
• Jens Klappstein
• Jürgen Dickmann
• Klaus Dietmayer

Keywords

• Vehicles
• Azimuth
• Sensor systems
• Doppler radar
• Estimation
• Standards
• Ego-motion Estimation
• Field Of View
• Covariance Matrix
• Azimuth Angle
• Motion State
• Motion Estimation
• Radar Sensor
• Yaw Rate
• Robot Localization
• Clutter Suppression
• Degrees Of Freedom
• Kalman Filter
• Velocity Profile
• Velocity Vector
• Position Error
• Monocular
• Motion Parameters
• Inertial Measurement Unit
• System Overview
• Target State
• Visual Odometry
• Azimuth Position
• Velocity Error
• Random Sample Consensus
• Stereopsis
• Consecutive Frames
• Advanced Driver Assistance Systems
• Inliers
• Sideslip Angle