Precise Estimation of Geo-location Uncertainty

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-13-M-1699
Agency Tracking Number: F131-153-1870
Amount: $149,797.00
Phase: Phase I
Program: SBIR
Awards Year: 2013
Solicitation Year: 2013
Solicitation Topic Code: AF131-153
Solicitation Number: 2013.1
Small Business Information
Systems & Technology Research
600 West Cummings Park, Suite 6500, Woburn, MA, -
DUNS: 964928464
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mark Keck
 Sr Member Technical Staff
 (781) 503-3302
Business Contact
 Melinda Wood
Title: Director, Business Operations
Phone: (603) 718-9800
Research Institution
ABSTRACT: Algorithms that perform accurate geo-registration of imagery captured from an aerial platform are becoming prevalent in the surveillance community. A universal shortcoming of these algorithms is their inability to provide an accurate estimate of the uncertainty of the solution. In this proposal we describe a solution for geo-registration based around the popular simultaneous localization and mapping (SLAM) approach. This solution includes a graphical model that captures the complex relationships between the input observations (2D keypoints extracted from the imagery and tracked frame-to-frame) and the hidden variables that are being estimated (the poses of the platform at each frame). The model also provides an explicit representation for the uncertainty in the poses of the aircraft and directly characterizes the noisy input observations as inliers or outliers. This model is combined with an efficient inference procedure that is able to overcome some inherent challenges to the problem, namely that the poses of the aircraft are not Euclidean (and therefore nontrivial to model) and the combinatorial nature of labeling each input as an inlier/outlier. We provide an outline for experiments on real and synthetic data and metrics to validate the accuracy of the uncertainty estimates from the algorithm. BENEFIT: The military currently employs geo-registration algorithms in a number of application areas (e.g. image-based navigation, targeting, situational awareness). Accurate uncertainty characterization in solutions from geo-registration algorithms will provide a measure of confidence in the result, and enables principled fusion of the resulting solution with measurements from other sensors/algorithms. This has direct impact on image-based navigation, as mentioned above, where the geo-registration solution could be used to aid the system in GPS-denied environments. Because the approach is an extension to the SLAM algorithm, it has direct commercial application to virtually any autonomous robotic deployment scenario, like emergency search and rescue and remote exploration.

* information listed above is at the time of submission.

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