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Long-range GPS-denied Aerial Inertial Navigation with LIDAR Localization

Garrett Hemann, Sanjiv Singh and Michael Kaess
Conference Paper, IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS, October, 2016

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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.

BibTeX Reference
title = {Long-range GPS-denied Aerial Inertial Navigation with LIDAR Localization},
author = {Garrett Hemann and Sanjiv Singh and Michael Kaess},
booktitle = {IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS},
month = {October},
year = {2016},