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Random sample consensus.
False matches are rejected using random sample consensus (RANSAC) algorithm.
Random sample consensus (RANSAC) is a well-known framework for dealing with outliers [2].
First, within the bundle adjustment, erroneous matches are usually discarded by the random sample consensus (RANSAC) [14] or its extensions, progressive sample consensus (PROSAC) [15], and preemptive RANSAC [16].
These symmetrical parts are typically modeled as primitive shapes (cylinders, spheres, cones, etc)., and fitted to noisy sensor data using sample consensus methods.
The best match between congruent sets is randomly found by following the RANdom SAmple Consensus (RANSAC) approach [9].
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This is achieved using a variant of Random Sampling Consensus (RANSAC), which identifies and separates the modes.
We use a method similar to the Random Sample and Consensus (RANSAC) algorithm.
In the line detection step, a lane model is chosen: a cascade of two random sample and consensus stages is applied to fit and extract lane markings.
Sample a consensus structure (S(t )) given an alignment (A(t −1)).
Two researchers carried out these manual reviews on randomly split portions of the sample with consensus meetings to resolve uncertainties.
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