Abstract: In order to mend the problem of FastSLAM algorithm，such as adopting fixed sample number which results in the low efficiency of the algorithm when the state uncertainty of mobile robot is quite high，and the sample depletion is brought on by the resample step，this paper adopts fuzzy logic to adjust the number of particles dynamically and only resamples when it is necessary.Theoretical analysis and simulation experiments show that the algorithm has higher estimation accuracy and consistency.

Key words: Fast Simultaneous Localization And Map building（SLAM）, resample, fuzzy logic

摘要： FastSLAM算法采用固定样本数目，当移动机器人状态不确定性很高时，算法效率较低，并且重采样步骤容易导致样本耗尽的问题，采用模糊逻辑来动态调整粒子数目，并采用自适应重采样只在需要时才采样。理论分析和仿真结果表明，改进后的算法具有更高的估计精度和更好的连贯性。

关键词: 快速同步定位与地图创建（FastSLAM）, 重采样, 模糊逻辑