A New Inlier Identification Scheme for Robust Estimation Problems

Common goal of many computer vision and robotics 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. To tackle these problems we propose a novel efficient sampling based method for the robust estimation of model parameters. The method is based on the observation that for each data point, the properties of the residual distribution with respect to the generated hypotheses reveal whether the point is an outlier or an inlier. The problem of inlier/outlier identification can then be formulated as a classification problem. The proposed method is demonstrated on motion estimation problems from image correspondences with a large percentage of outliers (70%) on both synthetic and real data and estimation of planar models from range data. The method is shown to be of an order of magnitude more efficient than currently existing methods and does not require a prior knowledge of the outlier ratio and the inlier threshold.