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.
- SourceAvailable from: Javier González-Jiménez[Show abstract] [Hide abstract]
ABSTRACT: In this work we address the problem of optimal Bayesian filtering for dynamic systems with observation models that cannot be approximated properly as any parameterized distribution. In the context of mobile robots this problem arises in localization and simultaneous localization and mapping (SLAM) with occupancy grid maps. The lack of a parameterized observation model for these maps forces a sample-based representation, commonly through Monte Carlo methods for sequential filtering, also called particle filters. Our work is grounded on the demonstrated existence of an optimal proposal distribution for particle filters. However, this optimal distribution is not directly applicable to systems with non-parametric models. By integrating ideas from previous works on adaptive sample size, auxiliary particle filters, and rejection sampling, we derive a new particle filter algorithm that enables the usage of the optimal proposal to estimate the true posterior density of a non-parametric dynamic system. This new filter is better suited, both theoretically and in practice, than previous approximate methods for indoor and outdoor localization and SLAM, as confirmed by experiments with real robots.The International Journal of Robotics Research 01/2010; 29:1726-1742. · 2.86 Impact Factor