一种基于遗传算法的FastSLAM2．0算法

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周武, 赵春霞. 一种基于遗传算法的FastSLAM2．0算法[J]. 机器人, 2009, 31(1): 25-32.. ZHOU Wu, ZHAO Chun-xia. A FastSLAM 2.0 Algorithm Based on Genetic Algorithm. ROBOT, 2009, 31(1): 25-32..

摘要 FastSLAM 2.0算法的重采样过程会带来"粒子耗尽"问题，为了改进算法的性能、提高估计精度，将FastSLAM 2.0算法与遗传算法相结合，提出了一种解决SLAM问题的方法——遗传快速SLAM算法．针对FastSLAM 2.0算法的特点，设计了一种改进的遗传算法来兼顾粒子权值和粒子集的多样性．遗传快速SLAM算法采用unscented粒子滤波器估计机器人的路径，地图估计则采用扩展卡尔曼滤波器．采用SLAM领域的标准数据集"car park dataset"对提出的算法进行了验证，实验结果表明遗传快速SLAM算法在估计精度和一致性方面都具有较好的性能，并且算法的计算复杂度能满足实时性要求．

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	周武
	赵春霞

关键词 ：同时定位与地图创建, 遗传算法, 粒子滤波器, unscented卡尔曼滤波器, 扩展卡尔曼滤波器

Abstract：Resampling process often causes the"sample impoverishment"problem in FastSLAM 2.0.In order to improve the algorithm performance and to increase the estimation accuracy,FastSLAM 2.0 is combined with genetic algorithm,and a solution named"Genetic FastSLAM 2.0"is presented for the SLAM problem.Based on the specialty of FastSLAM 2.0, an improved genetic algorithm is designed with attention to both the particle weight and the samples’ diversity.Genetic FastSLAM 2.0 estimates the robot path with unscented particle filter (UPF),and the map with extended Kalman filter (EKF). Experiments are carded out with a benchmark dataset named"car park dataset"to evaluate performance of the genetic Fast- SLAM 2.0,and the results indicate that the genetic FastSLAM 2.0 performs well on both estimation accuracy and consistency, and the computational complexity satisfies the requirements from real-time applications.

Key words： simultaneous localization and map building (SLAM) genetic algorithm particle filter unscented Kalman filter (UKF) extended Kalman filter (EKF)

收稿日期: 2008-07-16

TP24

作者简介: 周武（1982- ），男，博士生．研究领域：同时定位与地图创建，机器人自主导航．
赵春霞（1946- ），女，博士，教授．研究领域：模式识别，智能机器人．

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https://robot.sia.cn/CN/ 或 https://robot.sia.cn/CN/Y2009/V31/I1/25

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