Radar odometry with recursive-RANSAC

This paper explores the use of radar odometry for Global Positioning System-denied navigation. The range progression from arbitrary ground-based point scatterers is used to estimate an unmanned aerial vehicle's relative motion. In high clutter environments, the recursive-RANSAC algorithm provides robust and efficient feature identification, data association, and tracking. The tracked feature range measurements are integrated with inertial measurement unit measurements in an extended Kalman filter. Real flight data from a cluttered environment are used to validate the results.

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