Abstract
MINPRAN, a new robust operator, finds good fits in data sets where
more than 50% of the points are outliers. Unlike other techniques that
handle large outlier percentages, MINPRAN does not rely on a known error
bound for the good data. Instead it assumes that the bad data are
randomly (uniformly) distributed within the dynamic range of the sensor.
Based on this, MINPRAN uses random sampling to search for the fit and
the number of inliers to the fit that are least likely to have occurred
randomly. It runs in time O(N2+SNlogN), where S is the number
of random samples and N is the number of data points. We demonstrate
analytically and experimentally that MINPRAN distinguishes good fits
from fits to random data, and that MINPRAN finds accurate fits and
nearly the correct number of inliers, regardless of the percentage of
true inliers
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