[1]房芳,马旭东,戴先中.基于混合模型的移动机器人同时定位与环境建模[J].东南大学学报(自然科学版),2009,39(5):923-927.[doi:10.3969/j.issn.1001-0505.2009.05.011]
 Fang Fang,Ma Xudong,Dai Xianzhong.Mixed-model based simultaneous localization and mapping approach for mobile robot[J].Journal of Southeast University (Natural Science Edition),2009,39(5):923-927.[doi:10.3969/j.issn.1001-0505.2009.05.011]
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基于混合模型的移动机器人同时定位与环境建模()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
39
期数:
2009年第5期
页码:
923-927
栏目:
自动化
出版日期:
2009-09-20

文章信息/Info

Title:
Mixed-model based simultaneous localization and mapping approach for mobile robot
作者:
房芳 马旭东 戴先中
东南大学自动化学院, 南京 210096; 东南大学复杂工程系统测量与控制教育部重点实验室, 南京 210096
Author(s):
Fang Fang Ma Xudong Dai Xianzhong
School of Automation, Southeast University, Nanjing 210096, China
Key Laboratory of Measurement and Control of CSE of Ministry of Education, Southeast University, Nanjing 210096, China
关键词:
移动机器人 同时定位与环境建模 贝叶斯法则 扩展卡尔曼滤波器
Keywords:
mobile robot simultaneous localization and mapping Bayes’ rules extended Kalman filter
分类号:
TP242
DOI:
10.3969/j.issn.1001-0505.2009.05.011
摘要:
提出一种基于混合地图模型的融合声纳传感器观测信息与里程计信息的同时定位与环境建模(SLAM)方法.该方法用混合模型即栅格地图模型和直线特征地图模型表示环境地图.首先,采用三区域声纳模型以及贝叶斯法则构建栅格地图,并通过在空间和时间上融合不同时刻多个声纳传感器的信息提高地图精度.然后,引入霍夫变换提取直线特征,创建直线特征地图,并通过比较地图中直线段的方向相似性、共线性与交叠性,确定全局与局部地图是否匹配.最后,利用直线特征以及扩展卡尔曼滤波器(EKF),通过状态预测、观测预测、位姿更新3个阶段估计出机器人更新的位姿信息,校正构建的地图模型,从而实现机器人的同时定位与环境地图构建.仿真实验和真实环境实验验证了该算法的可行性与有效性.
Abstract:
A new simultaneous localization and mapping(SLAM)approach based on a mixed map model using sonar data and odometry information is presented. The mixed model composed of occupancy grids and line maps is utilized to represent the environment map. Firstly, three region models and Bayes’ rules are used to construct an occupancy grid map. The map precision is enhanced through fusing the information of several sonar sensors at different times. Then, the Hough transform is introduced to extract line features and the line feature maps are created. The local map and the global map are matched by comparing orientation, collinearity and overlap of the straight-line segment in the maps. Finally, the simultaneous localization and mapping are accomplished with the line features and extended Kalman filter through state prediction, observation prediction and estimation phase, which can estimate the robot pose and correct the map model. The simulation results and the real experimental results indicate the feasibility and validity of this approach.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 房芳(1980—),女,博士,讲师,ffang@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(60805032)、国家高技术研究发展计划(863计划)资助项目(2007AA041703, 2006AA040202).
引文格式: 房芳,马旭东,戴先中.基于混合模型的移动机器人同时定位与环境建模[J].东南大学学报:自然科学版,2009,39(5):923-927. [doi:10.3969/j.issn.1001-0505.2009.05.011]
更新日期/Last Update: 2009-09-20