Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography
by Fischler, Martin A. and Bolles, Robert C.
Communications of the ACM, vol. 24, no. 6, pp. 381-395, 1981.
A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus 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 this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with know locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented 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.
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| Name | Title | ||
|---|---|---|---|
| Bolles, Robert C | Program Director |  |
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| Fischler, Martin A | Principal Scientist | |
