融合渐消无迹粒子滤波与高斯重采样的FastSLAM 算法

摘要：为解决快速同步定位与地图构建算法因粒子退化导致SLAM(simultaneous location and mapping)估计精度 不佳的问题，提出一种融合渐消自适应无迹粒子滤波与高斯分布重采样的FastSLAM 算法。通过融合渐消滤波和无 迹粒子滤波，产生一种自适应提议分布，利用高斯分布对高权重粒子进行分散得到新粒子。建立机器人运动模型和 观测模型，并在仿真环境中进行性能验证。仿真结果表明：该算法能有效地缓解粒子退化，增加系统稳定性，提高 SLAM 估计精度。

FastSLAM Algorithm Based on Combining Fading Unscented Particle Filtering and Gaussian Re-sampling

To solve the low estimating accuracy of SLAM (simultaneous location and mapping) caused by particle degradation in fast simultaneous location and mapping algorithm, a FastSLAM algorithm which combine fading adaptive unscented particle filtering and Gaussian distributed re-sampling is proposed. An adaptive proposal distribution was generated by combining fading filtering and unscented particle filtering, and the high-weight particles were dispersed by Gaussian distribution to get new particles. The motion model and observation model of robot were established, and the performance was tested in simulation environment. The simulation result shows that, the algorithm can effectively alleviate particle degradation, increase system stability and improve SLAM estimation accuracy.