一种基于AMPF和FastSLAM的复合SLAM算法

为了改进快速同时定位和地图创建(FastSLAM)算法的粒子集性能、提高估计精度，提出基于AMPF和FastSLAM的复合SLAM算法.将辅助边缘粒子滤波器(AMPF)与FastSLAM架构相结合，用AMPF估计机器人位姿，单个粒子的位姿提议分布用无轨迹卡尔曼滤波估计.设计与AMPF和FastSLAM架构均兼容的采样方法和粒子数据结构，在FastSLAM框架下用扩展卡尔曼滤波递归估计地图.实验表明，该算法的粒子集性能比FastSLAM 2.0算法好，并且它的位姿估计精度高于FastSLAM 2.0算法.此外，粒子数较少时，该算法的估计精度较高，从而可适当减少粒子数目来提高算法的计算效率.

An AMPF and FastSLAM Based Compositive SLAM Algorithm

An AMPF and FastSLAM Based Compositive SLAM algorithm is presented to improve the performance of samples and increase the estimation accuracy. The auxiliary marginal particle filter (AMPF) is combined with the FastSLAM framework. In the proposed algorithm, the robot pose is estimated by AMPF, and the proposal pose distribution of each particle is estimated by UKF. Appropriate sampling strategy and particle data structure are designed to be compatible with both AMPF and FastSLAM framework. The map is estimated recursively by EKF in the FastSLAM framework. Experimental results indicate that the samples'performance of the proposed algorithm is better than that of FastSLAM 2.0, and the estimation accuracy of the proposed algorithm is higher than that of FastSLAM 2.0. Moreover, the estimation accuracy of the proposed algorithm is good with few samples. Therefore, it is feasible to improve the computational efficiency by reducing the number of samples.