Adaptive dynamic clustered particle filtering for mobile robots global localization

This article presents an adaptive dynamic clustered particle filtering method for mobile robot global localization. The posterior distribution of robot pose in global localization is usually multimodal due to the symmetry of the environment and ambiguous detected features. Moreover, the multimodal distribution of the posterior varies as the robot moves and observations be obtained. Considering these characteristics, we use a set of clusters of particles to represent the posterior. These clusters are dynamically evolved corresponding to the varying posterior by merging the overlapping clusters and splitting the diffuse clusters or those whose particles gather to some sub-clusters inside. Further, in order to improve computational efficiency without sacrificing estimation accuracy, a mechanism for adapting the sample size of clusters is proposed. The theoretical lower bound of the number of particles needed to limit the estimation error is derived, based on the central limit theorem in multi-dimensional space and the statistic theory of Importance Sampling. Then, a method for tuning the sample size for each cluster according to the derived lower bound is presented. Experiment results show the effectiveness of the proposed method, which is sufficient to achieve robust tracking of robot's real pose and meanwhile significantly enhance the computational efficiency.