Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

  • Authors:
  • Martin A. Fischler;Robert C. Bolles

  • Affiliations:
  • SRI International, Menlo Park, CA;SRI International, Menlo Park, CA

  • Venue:
  • Communications of the ACM
  • Year:
  • 1981

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Abstract

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 known 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