Beyond RatSLAM: Improvements to a biologically inspired SLAM system

A SLAM algorithm inspired by biological principles has been recently proposed and shown to perform well in a large and demanding scenario. We analyse and compare this system (RatSLAM) and the established Bayesian SLAM methods and identify the key difference to be an additive update step. Using this insight, we derive a novel filter scheme and successfully show that it can entirely replace the core of the RatSLAM system while maintaining its desirable robustness. This leads to a massive speedup, as the novel filter can be calculated very efficiently. We successfully applied the new algorithm to the same 66 km long dataset that was used with the original algorithm.