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“Long-range GPS-denied Aerial Inertial Navigation” by G. Hemann. Masters thesis, Carnegie Mellon University, May 2016. CMU-RI-TR-16-11.
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.
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BibTeX entry:
@mastersthesis{Hemann16thesis_ms,
author = {G. Hemann},
title = {Long-range {GPS}-denied Aerial Inertial Navigation},
school = {Carnegie Mellon University},
month = may,
year = {2016},
note = {CMU-RI-TR-16-11}
}