The causal update filter — A novel biologically inspired filter paradigm for appearance-based SLAM

Abstract

Recently a SLAM algorithm based on biological principles (RatSLAM) has been proposed. It was proven to perform well in large and demanding scenarios. In this paper we establish a comparison of the principles underlying this algorithm with standard probabilistic SLAM approaches and identify the key difference to be an additive update step. Using this insight, we derive the novel, non-Bayesian Causal Update filter that is suitable for application in appearance-based SLAM. We successfully apply this new filter to two demanding vision-only urban SLAM problems of 5 and 66 km length. We show that it can functionally replace the core of RatSLAM, gaining a massive speed-up.