RTM框架下基于分层拓扑结构的多机器人系统地图拼接

doi:10.3724/SP.J.1218.2013.00292

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引用本文:
贾松敏, 李雨晨, 王可, 李秀智, 郭兵. RTM框架下基于分层拓扑结构的多机器人系统地图拼接[J]. 机器人, 2013, 35(3): 292-298.DOI: 10.3724/SP.J.1218.2013.00292. JIA Songmin, LI Yuchen, WANG Ke, LI Xiuzhi, GUO Bing. Map Merging for Multi-robot Systems Based on Hierarchical Topology Structure under RTM Framework. ROBOT, 2013, 35(3): 292-298. DOI: 10.3724/SP.J.1218.2013.00292.

摘要

针对大规模的未知环境，在机器人中间件技术（RTM）框架下，提出了一种基于视觉特征的拓扑地图节点匹配方法，并结合局部扫描匹配策略，实现多机器人系统地图拼接．该方法首先建立主-辅结构的多机器人模型，利用改进的SP²ATM算法建立未知环境的拓扑结构并增量式地创建地图．在此基础上，提出了融合尺度不变（SIFT）特征的分层拓扑地图结构，并结合迭代最近点算法来实现多机器人系统地图拼接．本文以RTM作为通讯平台，使系统具有较高的实时性、灵活性和鲁棒性． USARSim仿真平台与真实环境下的实验结果验证了所提方法的可行性与有效性．

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关键词 ：多机器人系统, 机器人中间件技术, 地图拼接, 尺度不变特征, 迭代最近点算法

Abstract：

For large-scale unknown environments, a method of topological node matching based on visual feature is presented, and a local scan matching strategy is integrated to realize map merging for multi-robot system under RTM (robot technology middleware) framework. A main-auxiliary structure model of multiple robots is developed, and an improved SP²ATM algorithm is adopted to incrementally constructing topological map in unknown environments. Based on this, the hierarchical topology structure including SIFT (scale-invariant feature transform) feature information is presented, which is combined with ICP (iterative closest point) algorithm to realize map merging of multi-robot systems. RTM is taken as communication platform to improve the realtime performance, flexibility and robustness of the system. Simulation on USARSim and experimental results in actual environments verify the effectiveness of the proposed method.

Key words： multi-robot system RTM (robot technology middleware) map merging SIFT (scale-invariant feature transform) ICP (iterative closest point)

收稿日期: 2012-10-22 录用日期: 2013-03-25

TP24

基金资助:

北京市自然科学基金重点资助项目（KZ201110005004）；国家自然科学基金资助项目（61175087，61105033）；国家教育部留学回国人员科研启动基金资助项目.

通讯作者: 贾松敏，jsm@bjut.edu.cn. E-mail: jsm@bjut.edu.cn.

作者简介: 贾松敏（1964-），女，博士，教授．研究领域：智能服务机器人，机器人分散控制，计算机视觉．
李雨晨（1987-），男，硕士生．研究领域：模式识别与智能系统．
王可（1988-），男，博士生．研究领域：模式识别与智能系统，机器视觉．

链接本文:

https://robot.sia.cn/CN/10.3724/SP.J.1218.2013.00292 或 https://robot.sia.cn/CN/Y2013/V35/I3/292

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