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“Long-range GPS-denied Aerial Inertial Navigation with LIDAR Localization” by G. Hemann, S. Singh, and M. Kaess. In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, (Daejeon, Korea), Oct. 2016, pp. 1659-1666.
Despite significant progress in GPS-denied autonomous flight, long-distance traversals (> 100 km) in the absence of GPS remain elusive. This paper demonstrates a method capable of accurately estimating the aircraft state over a 218 km flight with a final position error of 27 m, 0.012% of the distance traveled. Our technique efficiently captures 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 two flight 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:
@inproceedings{Hemann16iros,
author = {G. Hemann and S. Singh and M. Kaess},
title = {Long-range {GPS}-denied Aerial Inertial Navigation with
{LIDAR} Localization},
booktitle = {Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and
Systems, IROS},
pages = {1659-1666},
address = {Daejeon, Korea},
month = oct,
year = {2016}
}