【摘要】 面向大尺度环境中的移动机器人同时定位与地图构建(Simultaneous localization and mapping,SLAM)问题,提出平方根容积Rao-Blackwillised粒子滤波SLAM算法.算法主要特点在于:1)采用容积律计算SLAM中的非线性函数高斯权重积分,达到减小SLAM非线性模型线性化误差、提高SLAM精度的目的;2)在SLAM中直接传播误差协方差矩阵的平方根因子,避免了耗费时间的协方差矩阵分解与重构过程,提高了SLAM计算效率.通过仿真、实验将提出的SLAM算法与FastSLAM2.0、UFastSLAM两种算法进行对比,结果表明本文算法在SLAM性能上优于另两者.更多还原

【Abstract】 In this paper, we derive a new large-scale environment simultaneous localization and mapping(SLAM) algorithm based on square-root cubature Rao-Blackwillised particle filter. The main contributions are: 1) to enhance the SLAM performance, the effective cubature rule is utilized to calculate the Gaussian weighted integral of the nonlinear function; 2) the covariance square-root factors are directly propagated in our SLAM process. Hence, the time-expensive decompositions on covariance matrixes are avoided. The performance of the proposed algorithm is compared with FastSLAM2.0 and UFastSLAM using a serial simulations and experiments. Results show that the proposed SLAM outperforms FastSLAM2.0 and UFastSLAM.更多还原