基于分布式无味边缘粒子滤波的同步定位与地图构建

Distributed Unscented Marginalized Particle Filter for Simultaneous Localization and Mapping

针对复杂环境下同步定位与地图构建(SLAM)中分布式粒子滤波算法存在计算量大、粒子退化严重的问题,在分布式算法的基础上结合无味粒子滤波和边缘化算法,提出了一种基于分布式无味边缘粒子滤波的算法.该算法依据分布式思想将系统分解为多个仅包含部分状态量的子系统,各子系统均采用无味粒子滤波算法进行状态估计,通过边缘化算法优化无味粒子滤波算法的边缘分布函数,主滤波器融合各子滤波器的数据计算最终结果,克服了滤波精度低、计算复杂度高的问题.最后,通过仿真试验证明改进的分布式边缘粒子滤波算法能够抑制粒子退化现象,具有较好的实时性和滤波精度,是解决SLAM的新的有效方法.

Aimed at the problems of low precision, large amount of calculation and severe sample degeneracy of simultaneous localization and mapping（SLAM）, this paper presented a distributed unscented marginalized particle filter（DUMPF） algorithm based on the combination of the distributed unscented particle fil- ter（DUPF） with the marginalized particle filter（MPF）. In the proposed method, the SLAM system was divided into several subsystems according to the distribution algorithms. The unscented particle filter （UPF） was used in each subsystem to estimate a part of the states. The marginal distribution of the UPF was optimized to reduce the computational complexity. The estimated results of the subsystems were transmitted to the master filter to obtain the final result. The simulation results showed that the improved DUMPF could prevent the particle degeneration problem, and had a higher precision and a smaller computational complexity.