1-Point RANSAC

Abstract

Random Sample Consensus (RANSAC) has become one of the most successful techniques for robust estimation from a data set that may contain outliers. It works by constructing model hypotheses from random minimal data subsets and evaluating their validity from the support of the whole data. In this chapter we present an efficient RANSAC algorithm for an Extended Kalman Filter (EKF) framework that uses the available prior probabilistic information from the EKF in the RANSAC model hypothesize stage. This allows the minimal sample size to be reduced to one, resulting in large computational savings without performance degradation. 1-Point RANSAC is also shown to outperform both in accuracy and computational cost the Joint Compatibility Branch and Bound (JCBB) algorithm, a gold-standard technique for spurious rejection within the EKF framework. The combination of this 1-point RANSAC and the robocentric formulation of the EKF SLAM allows a qualitative jump on the general performance of the algorithms presented in this book: In this chapter, sequences covering trajectories of several hundreds of metres are processed showing highly accurate camera motion estimation results.