Techniques for inferring terrain parameters related to ground vehicle mobility using UAV born IFSAR and lidar data

Phillip J. Durst; Alex Baylot; Burney McKinley

doi:10.1117/12.883510

25 May 2011 Techniques for inferring terrain parameters related to ground vehicle mobility using UAV born IFSAR and lidar data

Phillip J. Durst, Alex Baylot, Burney McKinley

Author Affiliations +

Proceedings Volume 8020, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VIII; 80200Z (2011) https://doi.org/10.1117/12.883510
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Predicting ground vehicle performance requires in-depth knowledge, captured as numeric parameters, of the terrain on which the vehicles will be operating. For off-road performance, predictions are based on rough terrain ride comfort, which is described using a parameter entitled root-mean-square (RMS) surface roughness. Likewise, on-road vehicle performance depends heavily on the slopes of the individual road segments. Traditional methods of computing RMS and road slope values call for high-resolution (inch-scale) surface elevation data. At this scale, surface elevation data is both difficult and time consuming to collect. Nevertheless, a current need exists to attribute large geographic areas with RMS and road slope values in order to better support vehicle mobility predictions, and high-resolution surface data is neither available nor collectible for many of these regions. On the other hand, meter scale data can be quickly and easily collected for these areas using unmanned aerial vehicle (UAV) based IFSAR and LIDAR sensors. A statistical technique for inferring RMS values for large areas using a combination of fractal dimension and spectral analysis of five-meter elevation data is presented. Validation of the RMS prediction technique was based on 43 vehicle ride courses with 30-centimeter surface elevation data. Also presented is a model for classifying road slopes for long road sections using five-meter elevation data. The road slope model was validated against one-meter LIDAR surface elevation profiles. These inference algorithms have been successfully implemented for regions of northern Afghanistan, and some initial results are presented.

Citation Download Citation

Phillip J. Durst, Alex Baylot, and Burney McKinley "Techniques for inferring terrain parameters related to ground vehicle mobility using UAV born IFSAR and lidar data", Proc. SPIE 8020, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VIII, 80200Z (25 May 2011); https://doi.org/10.1117/12.883510

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Roads

Data modeling

Unmanned aerial vehicles

LIDAR

Interferometric synthetic aperture radar

Sensors

Surface roughness

Show All Keywords

Keywords/Phrases

Search In:

Publication Years