Research Papers

Correcting INS Drift in Terrain Surface Measurements

[+] Author and Article Information
Heather M. Chemistruck, Robert Binns

 Virginia Polytechnic Institute and State University, 150 Slayton Avenue, Danville, VA 24540

John B. Ferris

 Virginia Polytechnic Institute and State University, 150 Slayton Avenue, Danville, VA 24540jbferris@vt.edu

J. Dyn. Sys., Meas., Control 133(2), 021009 (Mar 09, 2011) (8 pages) doi:10.1115/1.4003098 History: Received January 29, 2010; Revised August 02, 2010; Published March 09, 2011; Online March 09, 2011

Modern terrain measurement systems use an inertial navigation system (INS) to measure and remove vehicle movement from laser measurements of the terrain surface. Instrumental and environmental biases inherent in the INS produce noise and drift errors in these measurements. The evolution and implications of terrain surface measurement techniques and existing methods for correcting INS drift are reviewed as a framework for a new compensation method for INS drift in terrain surface measurements. Each measurement is considered a combination of the true surface and the error surface, defined on a Hilbert vector space, in which the error is decomposed into drift (global error) and noise (local error). The global and local subspaces are constructed such that the drift is modeled as a random walk process and the noise is a zero-mean process. This theoretical development is coupled with careful experimental design to identify the drift component of error and discriminate it from true terrain surface features, thereby correcting the INS drift. It is shown through an example that this new compensation method dramatically reduces the variation in the measured surfaces to within the resolution of the measurement system itself.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Sample of GPS test results: (a) static test results and (b) dynamic test results

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Figure 2

Creating a uniform grid (curved regular grid) in the horizontal plane: (a) cloud data measured in vehicle centered coordinate system and (b) curved regular grid example

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Figure 3

Defining the surface coordinate system (u,v,zi,k) in a CRG format

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Figure 8

Estimated noise vectors (q=1 and i=300)

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Figure 9

Longitudinal profiles (q=1 and j=25)

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Figure 10

Estimated noise vectors (q=2 and i=300)

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Figure 11

Longitudinal profiles (q=2 and j=25) and estimated true surface

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Figure 12

Signal processing overview: data collection and filtering

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Figure 4

Plot of first 20 singular values

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Figure 5

Plot of first two basis vectors

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Figure 6

Estimated noise vectors (q=0 and i=300)

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Figure 7

Longitudinal profiles (q=0 and j=25)




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