一种改进的粒子滤波SLAM算法

An Improved Particle Filter SLAM Algorithm

传统的粒子滤波SLAM算法中,由于历史信息未被利用而导致估计精度较低.文中结合精确稀疏滞后状态信息滤波具有自然稀疏的信息矩阵因而估计精度高以及精确稀疏扩展信息滤波计算效率高的优点,将二者混合应用于粒子滤波SLAM算法中.不但充分应用信息矩阵记录的机器人位姿与特征间关系的历史信息从而提高估计的精度,而且克服机器人转动状态及环境特征疏密带来的应用缺陷.仿真与真实机器人实验的实验结果均表明文中算法的有效性与可行性.

The estimation accuracy of the conventional particle filter algorithm is low because the historical information is not fully utilized. Combining the high estimation accuracy of exactly sparse delayed-state filter（ESDF） and the high efficiency of exactly sparse extended information filter（ ESEIF）, an improved particle filter SLAM algorithm is proposed. In this algorithm, the information matrix of ESDF, maintaining the historical relationship of robot pose and characteristics, improves the accuracy of the estimate, and ESEIF overcomes the defects of robot rotational state and characteristics density. Results of both emulational and factual experiments show that the proposed algorithm is valid and feasible.