Robust Real-Time SFM for SMAV

Award Information
Agency:
Department of Defense
Branch
Defense Advanced Research Projects Agency
Amount:
$99,000.00
Award Year:
2005
Program:
STTR
Phase:
Phase I
Contract:
W31P4Q-05-C-0154
Award Id:
74353
Agency Tracking Number:
05ST1-0039
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
8001 Lingay Drive, Allison Park, PA, 15101
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
077753114
Principal Investigator:
Takeo Kanade
Professor of CS and Robotics
(412) 268-3016
Takeo.Kanade@cs.cmu.edu
Business Contact:
P. Balakumar
CEO
(800) 684-2772
bala@etovia.com
Research Institute:
CARNEGIE MELLON UNIV.
Deborah D Harvard
Robotics Institute - NSH 3103, 5000 Forbes Avenue
Pittsburgh, PA, 15213
(412) 268-2996
Nonprofit college or university
Abstract
We shall investigate and implement robust Real-Time SFM for SMAV application scenarios. Automatic pose determination and extraction of 3D structure of the environment are critical to autonomous navigation and obstacle avoidance of SMAVs in constrained and crowded environments. Extracting such state information under various constraints (low quality video, sensor payload limitations, minimal onboard processing capabilities, sudden and abrupt motions changes etc.) pose a significant challenge and require development of innovative and robust algorithms. We will 1) Develop and implement SFM algorithms with robustness at each processing stage to ensure reliable performance with low quality video input, and to detect degenerate or near degenerate cases to avoid erroneous estimation; 2) Perform extensive feasibility study of our proposed algorithm using data with progressive levels of fidelity including a) Simulated data with ground truth information b) Motion-captured data and c) Data collected from real SMAVs equipped with miniaturized cameras; and 3) Systematically evaluate the performance of our SFM algorithm for reconstruction accuracy at different flying patterns in various environments, and different depth ranges that can be recovered by our algorithm. The objective is to develop a practical and field deployable system that will provide navigation and obstacle detection capabilities to the SMAV platforms.

* information listed above is at the time of submission.

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