摘要
研究了移动机器人同时定位与地图创建(SLAM)精确稀疏扩展信息滤波(ESEIF)的地图优化算法,即利用信息熵度量变量不确定性的特性来对地图特征点进行分类,选择不同类型的特征点处理ESEIF的不同更新过程,同时优化活动地图,使SLAM更新在恒定时间内实现,且提高了机器人和地图的估计精度.仿真实验证明:在特征点多的大环境下,特征点优化后的算法实时性强,估计精确度更高.
The Algorithm of map optimization about exactly sparse extended information filter（ESEIF） is investigated based on simultaneous localization and mapping（SLAM）.It classifies the features of map exploiting the characteristic of information entropy which measure uncertainty of the variable,and choose different features to update corresponding process during ESEIF,and optimize the active maps at the same time.It updates SLAM in constant time,and it increases the estimation accuracy of the robot and map.Finally,the simulation proved,the algorithm can estimate in higher real-time and accuracy in large environment of considerable numerous features.
出处
《宁波大学学报（理工版）》
CAS
2011年第2期46-50,共5页
Journal of Ningbo University：Natural Science and Engineering Edition
关键词
同时定位与地图构建
精确稀疏扩展信息滤波
信息熵
活动地图
simultaneous localization and mapping
exactly sparse extended information filter
information entropy
active maps
