Monte Carlo Sampling of Non-Gaussian Proposal Distribution in Feature-Based RBPF-SLAM

Particle filters are widely used in mobile robot localization and mapping. It is well-known that choosing an appropriate proposal distribution plays a crucial role in the success of particle filters. The proposal distribution conditioned on the most recent observation, known as the optimal proposal distribution (OPD), increases the number of effective particles and limits the degeneracy of filter. Conventionally, the OPD is approximated by a Gaussian distribution, which can lead to failure if the true distribution is highly non-Gaussian. In this paper we propose two novel solutions to the problem of feature-based SLAM, through Monte Carlo approximation of the OPD which show superior results in terms of mean squared error (MSE) and number of effective samples. The proposed methods are capable of describing non-Gaussian OPD and dealing with nonlinear models. Simulation and experimental results in large-scale environments show that the new algorithms outperform the aforementioned conventional methods.