Robust Square-Root Cubature FastSLAM with Genetic Operators

An improved FastSLAM based on the robust square-root cubature Kalman filter (RSRCKF) with partial genetic resampling is proposed in this paper. In the proposed method, RSRCKF is used to design the proposal distribution of FastSLAM and to estimate environment landmarks. The proposed method does not require a priori knowledge of the noise statistics. In addition, to increase diversity, it uses the genetic operators-based strategy to further improve the particle diversity. In fact, a partial genetic resampling operation is carried out to maintain the diversity of particles. The proposed method is compared with other methods via simulation and experimental data. It can be seen from the results that the proposed method provides significantly more accurate and robust estimation results compared with other methods even with fewer particles and unknown a priori. In addition, the consistency of the proposed method is better than that of other methods.

Automated summary

An improved FastSLAM method based on RSRCKF with partial genetic resampling is proposed in this paper. The method does not require prior knowledge of noise statistics and utilizes genetic operators to maintain particle diversity, resulting in significantly more accurate and robust estimation results compared to other methods. Additionally, the proposed method demonstrates better consistency than existing methods in simulation and experimental data comparisons.