A new inlier identification scheme for robust estimation problems

algorithms is to extract geometric information from the sensory data. Due to noisy measurements and errors in matching or segmentation, the available data are often corrupted with outliers. In such instances robust estimation methods are employed for the problem of parametric model estimation. In the presence of a large fraction of outliers sampling based methods are often the preferred choice. Traditionally used RANSAC algorithm however requires a large number of samples, prior knowledge of the outlier ratio and an additional, difficult to obtain, inlier threshold for hypothesis evaluation.