Light Detection and Ranging (LIDAR) Surface Feature Extraction Tool

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$1,000,000.00
Award Year:
2006
Program:
SBIR
Phase:
Phase II
Contract:
N61339-06-C-0055
Agency Tracking Number:
N043-245-0533
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
VISUAL LEARNING SYSTEMS, INC.
P.O. Box 8226, Missoula, MT, 59807
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
150373442
Principal Investigator:
Stuart Blundell
COO
(406) 829-1384
sblundell@vls-inc.com
Business Contact:
David Opitz
CEO
(406) 829-1384
opitz@vls-inc.com
Research Institution:
n/a
Abstract
NAVAIR requires accurate and timely 3-D geospecific features, terrain, and imagery data to support Modeling and Simulation (M&S) software applications used in training and pre-mission rehearsal simulators. LIDAR data provides an excellent source for spatially accurate digital terrain and 3-D feature information; however, current visual database production techniques require hundreds of man-hours to extract and attribute features. Automated processes are necessary to achieve the Navy's goal of good M&S data that is efficiently processed and correctly formatted. Bottlenecks in producing good data are a result of manual processes used in (1) registering old features and imagery to newly acquired imagery and LIDAR, (2) extracting and editing complex 3-D geometries, and (3) updating and attributing features. The proposed Phase II workplan promises to significantly alleviate these bottlenecks by developing a system that exploits the information content of LIDAR and multiband imagery using machine learning-based feature extraction technology. The envisioned system will have the capability of automating the extraction of M&S features from LIDAR, registering features to images, allowing dynamic interaction with analysts, interrogating analysts when it needs additional information, and embedding the ability to create a continuously updated repository of feature extraction models.

* information listed above is at the time of submission.

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