摘要
在移动机器人同时定位与地图创建(SLAM)过程中,机器人本身位置不确定,其所处环境也不可预知,针对这些不确定性因素,应用贝叶斯规则作为理论基础,建立移动机器人SLAM算法的概率表示模型,通过扩展卡尔曼滤波器实现SLAM算法,并介绍一种激光雷达数据与特征地图的数据关联方法。实验结果表明,该方法为实现SLAM算法提供了一种有效可靠的途径。
During the mobile robot Simultaneous Localization And Mapping(SLAM),the location is unknown and the environment round is also unpredictable. Aiming at these uncertain factors,the Bayes rule is as a theory foundation,the probability model of the mobile robot SLAM is founded,the realization process of the SLAM by Extended Kalman Filter(EKF) is discussed. A data association method between the laser radar and the feature map is introduced. Experimental results show this method is effective and reliable to realize SLAM.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第2期31-32,41,共3页
Computer Engineering
基金
国家自然科学基金资助项目(60705020)
国家部委基金资助项目
关键词
机器人
概率论
同时定位与地图创建
扩展卡尔曼滤波器
robot
probability theory
Simultaneous Localization And Mapping(SLAM)
Extended Kalman Filter(EKF)