Advanced Bayesian Methods for Lunar Surface Navigation

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$99,937.00
Award Year:
2010
Program:
SBIR
Phase:
Phase I
Contract:
NNX10CC95P
Agency Tracking Number:
094663
Solicitation Year:
2009
Solicitation Topic Code:
O4.03
Solicitation Number:
n/a
Small Business Information
Autonomous Exploration, Inc.
385 High Plain Road, Andover, MA, 01810-3234
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
961415887
Principal Investigator:
Julian Center
Principal Investigator
(978) 683-0290
jcenter@ieee.org
Business Contact:
Julian Center
CEO
(978) 269-4120
jcenter@ieee.org
Research Institution:
n/a
Abstract
The key innovation of this project will be the application of advanced Bayesian methods to integrate real-time dense stereo vision and high-speed optical flow with an Inertial Measurement Unit (IMU) to produce a highly accurate planetary rover navigation system. The software developed in this project will leverage current computing technology to implement advanced Visual Odometry (VO) methods that will accurately track much faster rover movements. Our fully Bayesian approach to VO will utilize more information from the images than previous methods are capable of using. Our Bayesian VO does not explicitly select features to track. Instead it implicitly determines what can be learned from each image pixel and weights the information accordingly. This means that our approach can work with images that have no distinct corners, which can be a significant advantage with low contrast images from permanently shadowed areas. We expect that the error characteristics of the visual processing with be complementary to the error characteristics of a low-cost IMU. Therefore, the combination of the two should provide highly accurate navigation.

* information listed above is at the time of submission.

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