摘要：

针对Gmapping算法在高相似度多闭环环境下出现的因粒子耗尽而无法精确定位的问题,提出一种融合萤火虫算法的Rao-Blackwellized粒子滤波器RBPF同步定位与地图构建优化算法。利用萤火虫算法提高粒子滤波器的估计能力,将采样后的粒子集移向高似然区域,改善粒子的分布,同时保证低似然粒子多样性以降低粒子贫乏的影响。在MIT和FHW数据集下的仿真结果表明,优化算法在不同的实验环境下能够建立更为精确的栅格地图,从而验证其有效性和可行性。

关键词: 同步定位与地图构建, 粒子滤波, Gmapping算法, 拓展卡尔曼滤波, 萤火虫算法

Abstract:

Aiming at the problem that Gmapping algorithm can not be accurately located due to particle depletion in high similarity and multiple closed-loop environment,a Rao-Blackwellized Particle Filtering(RBPF) Simultaneous Localization and Mapping(SLAM) optimization algorithm based on firefly algorithm is proposed.The firefly algorithm is used to improve the estimation ability of the particle filter,and the sampled particle set is moved to the high likelihood region to improve the particle distribution while ensuring the low likelihood particle diversity to reduce the effect of particle deficiency.Simulation results under MIT and FHW datasets show that the optimization algorithm establishes more accurate raster maps in different experimental environments,and verifies its validity and feasibility.

Key words: Simultaneous Localization and Mapping (SLAM), Particle Filtering(PF), Gmapping algorithm, Extended Kalman Filtering (EKF), firefly algorithm

中图分类号: 

• TP18