摘要
为了实现无人机在无GPS的矿井环境下进行自主飞行,达到无人机的精准定位,提出了基于Rao-Blackwellized粒子滤波器的快速同步定位与地图创建(fast simultaneous location and mapping,Fast SLAM)算法。首先设计了一种适用于矿井环境下的人工路标,建立起了无人机的SLAM算法数学模型,接着提出一种改进算法—PSOFast SLAM算法提高准确性,对无人机的位姿和路标位置进行估计,实现无人机的精准定位和地图绘制。最后对进行仿真实验,仿真结果证明PSOFast SLAM算法有效改善了Fast SLAM算法粒子退化的问题,提高了井下无人机定位精度。
In order to realize the autonomous flight of Unmanned Aerial Vehicle(UAV)in the mine environment without GPS and also precise positioning it,an algorithm called Fast Simultaneous Location and Mapping(FastSLAM)which is based on Rao-Blackwellized particle filter is proposed.Firstly,an artificial road sign suitable for underground mine was designed and the mathematical model of Simultaneous Location and Mapping(SLAM)algorithm for UAV was established.Then an improved algorithm called PSOFastSLAM was proposed to improve the accuracy,to estimate the pose of UAV and the position of guideposts and to achieve the accurate positioning and map building for UAV.Finally,the simulation experiment is simulated,simulation results show that PSOFastSLAM algorithm can improve the particle degeneration problem in FastSLAM algorithm effectively and improve the positioning accuracy of UAV.
作者
刘书池
杨维
LIU Shu-chi;YANG Wei(School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China)
出处
《哈尔滨理工大学学报》
CAS
北大核心
2018年第4期75-81,共7页
Journal of Harbin University of Science and Technology
基金
国家重点研发计划资助项目(2016YFC0801806)
国家自然科学基金(51474015
51274018)
关键词
同步定位与地图构建
无人机
FASTSLAM算法
粒子群优化
simultaneous localization and map building
unmanned aerial vehicle
fastSLAM algorithm
particle swarm optimization
