| 基于概率的移动机器人SLAM算法框架 |
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| 引用本文： | 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. |
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| 作者姓名： | 石杏喜 赵春霞 |
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| 作者单位： | 1. 南京理工大学理学院,南京,210094;南京理工大学计算机学院,南京,210094
2. 南京理工大学计算机学院,南京,210094 |
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| 基金项目： | 国家自然科学基金资助项目(60705020);;国家部委基金资助项目 |
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| 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。
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| 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 |
| 修稿时间： | |
SLAM Algorithm Framework of Mobile Robot Based on Probability |
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| SHI Xing-xi,ZHAO Chun-xia.SLAM Algorithm Framework of Mobile Robot Based on Probability[J].Computer Engineering,2010,36(2):31-32. |
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| Authors: | SHI Xing-xi ZHAO Chun-xia |
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| Affiliation: | (1. College of Sciences, Nanjing University of Science and Technology, Nanjing 210094;2. College of Computer, Nanjing University of Science and Technology, Nanjing 210094) |
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| 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. |
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| Keywords: | robot probability theory Simultaneous Localization And Mapping(SLAM) Extended Kalman Filter(EKF) |
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