机器人球面单径容积FastSLAM算法

A Robot Spherical Simplex-Radial Cubature FastSLAM Algorithm

针对标准Fast SLAM算法存在的雅可比矩阵的计算、线性化误差累积等问题,提出了一种球面单径容积Fast SLAM算法(SSRCFast SLAM).算法的特点在于使用3阶球面单径准则计算SLAM中的非线性高斯权重积分,以提高精度.所提算法利用球面单径容积粒子滤波进行路径估计,利用球面单径容积卡尔曼滤波来维护路标.通过仿真实验和维多利亚公园数据集实验将所提算法同Fast SLAM2.0、UFast SLAM和CFast SLAM进行对比.结果显示,所提算法在不同粒子数与噪声环境下的定位与建图能力均优于其他3种算法,且在粒子数目较少或环境干扰较大时优势更显著,验证了所提算法的优越性.

Standard FastSLAM algorithm suffers from the calculation of the Jacobian matrices and linearization error accumulation. To overcome these problems, a spherical simplex-radial cubature FastSLAM （SSRCFastSLAM） algorithm is proposed. The 3rd-degree spherical simplex-radial rule is utilized to calculate the nonlinear Gaussian weighted integral in order to improve SLAM accuracy. The proposed algorithm uses spherical simplex-radial cubature particle filter to estimate the path, and uses spherical simplex-radial cubature Kalman filter to maintain the landmarks. The performance of the proposed algorithm is compared with that of FastSLAM2.0, UFastSLAM and CFastSLAM through simulations and Victoria Park dataset. The results show that the proposed algorithm yields better localization and mapping ability than the other three algorithms with different particle numbers and noise levels, and the advantage is more significant when the number of particles is small or the environment disturbance is large, therefore the superiority of the proposed algorithm is verified.