Limits to the Consistency of Ekf-based Slam

This paper analyzes the consistency of the classical extended Kalman filter (EKF) solution to the simultaneous localization and map building (SLAM) problem. Our results show that in large environments the map quickly becomes inconsistent due to linearization errors. We propose a new EKF-based SLAM algorithm, robocentric mapping, that greatly reduces linearization errors, improving map consistency. We also present results showing that large-scale mapping methods based on building local maps with a local uncertainty representation (Tardós et al., 2002) have better consistency than methods that work with global uncertainties.