计算机工程 ›› 2010, Vol. 36 ›› Issue (2): 31-32.doi: 10.3969/j.issn.1000-3428.2010.02.011

• 博士论文 • 上一篇    下一篇

基于概率的移动机器人SLAM算法框架

石杏喜1,2,赵春霞2   

  1. (1. 南京理工大学理学院,南京 210094;2. 南京理工大学计算机学院,南京 210094)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-01-20 发布日期:2010-01-20

SLAM Algorithm Framework of Mobile Robot Based on Probability

SHI Xing-xi1,2, ZHAO Chun-xia2   

  1. (1. College of Sciences, Nanjing University of Science and Technology, Nanjing 210094;2. College of Computer, Nanjing University of Science and Technology, Nanjing 210094)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-01-20 Published:2010-01-20

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)