Unscented H-infinity filtering based simultaneous localization and mapping with evolutionary resampling

Unscented FastSLAM (UFastSLAM) is a framework for simultaneous localization and mapping (SLAM). However, UFastSLAM is inconsistent over time due to the loss of particle diversity that caused mainly by the particle depletion in resampling step and incorrect a priori knowledge of process and measurement noise. To overcome these problems, in this paper, H-infinity UFastSLAM (HUFastSLAM) with evolutionary resampling is proposed. The proposed method can work in unknown statistical noise and does not require a prior knowledge about the of the noise statistics. In addition, to increase diversity, the resampling process is done based on the differential evolution (DE). The proposed algorithm is evaluated on a benchmark dataset. The simulation and experimental results demonstrate the effectiveness of the proposed algorithm in different situation.