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.
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 FastSLAM 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.
出处
《机器人》
EI
CSCD
北大核心
2009年第1期25-32,共8页
Robot
