Adaptive SLAM algorithm with sampling based on state uncertainty

Abstract

Since the uncertainty of a robot state changes over time, proposed is an adaptive simultaneous localisation and mapping (SLAM) algorithm based on the Kullback-Leibler distance (KLD) sampling and Markov chain Monte Carlo (MCMC) move step. First, it can adaptively determine the number of required particles by calculating the KLD between the posterior distribution approximated by particles and the true posterior distribution at each step. Secondly, it introduces the MCMC move step to increase the particle variety. Both simulation and experimental results demonstrate that the proposed algorithm can obtain more robust and precise results by computing the number of required particles more accurately than previous algorithms.