Fixed-lag Sampling Strategies for Particle Filtering SLAM

Abstract

We describe two new sampling strategies for Rao-Blackwellized particle filtering SLAM. The strategies, called fixed-lag roughening and the block proposal distribution, both exploit "future" information, when it becomes available, to improve the filter's estimation for previous time steps. Fixed-lag roughening perturbs trajectory samples over a fixed lag time according to a Markov chain-Monte Carlo kernel. The block proposal distribution directly samples poses over a fixed lag from their fully joint distribution conditioned on all the available data. Our experimental results indicate that the proposed strategies, especially the block proposal, yield significant improvements in filter consistency and a reduction in particle degeneracies compared to standard sampling techniques such as the improved proposal distribution of FastSLAM 2.