Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography
SRI INTERNATIONAL MENLO PARK CA ARTIFICIAL INTELLIGENCE CENTER
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In this paper, the authors introduce a new paradigm, Random Sample Consensus RANSAC, for fitting a model to experimental data. RANSAC is capable of interpretingsmoothing data containing a significant percentage of gross errors, and thus is ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of the paper describes the application of RANSAC to the Location Determination Problem LDP Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, the authors derive new results on the minimum number of landmarks needed to obtain a solution, and present algorithms for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing and analysis conditions. Implementation details and computational examples also are presented.
- Cartography and Aerial Photography
- Target Direction, Range and Position Finding