MINPRAN: a new robust estimator for computer vision

Abstract

MINPRAN is a new robust estimator capable of finding good fits in data sets containing more than 50% 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 the bad data are randomly distributed within the dynamic range of the sensor. Based on this, MINPRAN uses random sampling to search for the fit and the inliers to the fit that are least likely to have occurred randomly. It runs in time O(N2+SN log N), where S is the number of random samples and N is the number of data points. We demonstrate analytically that MINPRAN distinguished good fits to random data and MINPRAN finds accurate fits and nearly the correct number of inliers, regardless of the percentage of true inliers. We confirm MINPRAN驴s properties experimentally on synthetic data and show it compares favorably to least median of squares. Finally, we apply MINPRAN to fitting planar surface patches and eliminating outliers in range data taken from complicated scenes.