SUMMARYAn 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.

中文翻译：

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具有遗传算子的鲁棒平方根立方 FastSLAM

摘要本文提出了一种基于具有部分遗传重采样的鲁棒平方根容积卡尔曼滤波器（RSRCKF）的改进FastSLAM。在所提出的方法中，RSRCKF 用于设计 FastSLAM 的提议分布并估计环境地标。所提出的方法不需要噪声统计的先验知识。此外，为了增加多样性，它使用基于遗传算子的策略来进一步提高粒子的多样性。实际上，进行了部分遗传重采样操作以保持粒子的多样性。通过仿真和实验数据将所提出的方法与其他方法进行了比较。从结果可以看出，与其他方法相比，即使在粒子较少和先验未知的情况下，所提出的方法也能提供更准确和稳健的估计结果。此外，该方法的一致性优于其他方法。