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A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus

2008; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-540-88688-4_37

1611-3349

Rahul Raguram, Jan‐Michael Frahm, Marc Pollefeys,

Robotics and Sensor-Based Localization

The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. There have been a number of recent efforts that aim to increase the efficiency of the standard RANSAC algorithm. Relatively fewer efforts, however, have been directed towards formulating RANSAC in a manner that is suitable for real-time implementation. The contributions of this work are two-fold: First, we provide a comparative analysis of the state-of-the-art RANSAC algorithms and categorize the various approaches. Second, we develop a powerful new framework for real-time robust estimation. The technique we develop is capable of efficiently adapting to the constraints presented by a fixed time budget, while at the same time providing accurate estimation over a wide range of inlier ratios. The method shows significant improvements in accuracy and speed over existing techniques.