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。
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
Abstract: 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.
Key words:
robot,
probability theory,
Simultaneous Localization And Mapping(SLAM),
Extended Kalman Filter(EKF)
石杏喜;赵春霞. 基于概率的移动机器人SLAM算法框架[J]. 计算机工程, 2010, 36(2): 31-32.
SHI Xing-xi; ZHAO Chun-xia. SLAM Algorithm Framework of Mobile Robot Based on Probability[J]. Computer Engineering, 2010, 36(2): 31-32.