Analysis of Resampling Process for the Particle Depletion Problem in FastSLAM

Abstract

The state-of-the-art FastSLAM has been shown to cause a particle depletion problem while performing simultaneous localization and mapping for mobile robots. As a result, it always produces over-confident estimates of uncertainty as time progresses. This particle depletion problem is mainly due to the resampling process in FastSLAM, which tends to eliminate particles with low weights. Therefore, the number of particles to perform loop-closure decreases, which makes the performance of FastSLAM degenerate. The resampling process has not been thoroughly analyzed even though it is the main reason for the particle depletion problem. In this paper, standard resampling algorithms (systematic residual and partial resampling), and a rank-based resampling applying genetic algorithms are analyzed using computer simulations. For the thorough analysis, several performance measures are used such as effective sample size, the number of distinct particles, root mean square (RMS) errors, and complexity. According to the simulation results, all resampling algorithms could not resolve the particle depletion problem.