Long-range GPS-denied Aerial Inertial Navigation

Abstract

Despite significant progress in GPS-denied autonomous flight, long-distance traversals (over 100 km) in the absence of GPS remain elusive. This work focuses on techniques that efficiently capture the full state dynamics of the air vehicle with semi-intermittent global corrections using LIDAR measurements matched against an a priori Digital Elevation Model (DEM). Using an error-state Kalman filter with IMU bias estimation, we are able to maintain a high-certainty state estimate, reducing the computation time to search over a global elevation map. A sub region of the DEM is scanned with the latest LIDAR projection providing a correlation map of landscape symmetry. The optimal position is extracted from the correlation map to produce a position correction that is applied to the state estimate in the filter. This method provides a GPS-denied state estimate for long-range drift-free navigation. We demonstrate this method on multiple data sets from a full-sized helicopter, showing significantly longer flight distances over the current state of the art.