基于服务任务导向的机器人地图构建研究
|
摘要
随着服务机器人越来越多地走入我们的工作和生活,给人提供的服务种类也越来越广,如端茶送水、清运垃圾、货物搬运、传送信件等,因此构建适用于机器人服务任务的环境模型成为当前地图构建研究的热点。机器人传统的地图建模都侧重于空间几何结构的表示,适用于导航,但没有考虑机器人服务工作的区域性特征和局部服务空间的复杂性,更没有与人交互的语义信息。要使服务机器人具有智能,应从人对空间环境的表示方式上获得启发,研究能理解人的语言信息的服务机器人环境空间表示方法。人对环境的认识是分层次进行的：对于大空间环境构建了走廊和房间概念,而人操作的对象——物品的位置与房间应有必然关联；单位房间内大物品的摆放位置为人提供区域路径规划依据；小的局部复杂空间中物品的搜寻和抓取依靠人的视觉获得的立体空间模型完成。因此,模仿人的空间模型构建模式,针对机器人的物品管理、物品移动、物品操作三级服务任务导向,构建室内未知环境的三层环境地图是本文的研究重点。基于全局语义、区域规划和局部描述的三层环境地图,形成三级递阶规划系统,使服务机器人既能获得未知环境的房间分割模型,确定房间的功能及物品与房间的归属关系,又能实现走廊、房间之间和房间内的路径规划和导航,同时建立其操作对象及其周边环境的三维地图,使机器人能基于服务任务的导向,按“语义”进行逻辑推理,确定“目标”及其位置,为以服务任务为驱动的基于分层环境地图构建模式的“智能”导航奠定基础。由于多机器人系统具有的可拆分性、可重构性、容错性和鲁棒性等优点,多机器人协作构建地图的研究逐渐展开。本文在对三层分级环境地图构建的基础上,也对多机器人协作中备受关注的探测策略和定位问题进行了初步研究和探讨。
本文的研究思路分四步展开。首先在总结分析当前环境地图构建的现状和问题基础上,提出基于服务任务导向的三层环境地图构建模式：反映房间拓扑关系的全局语义地图；描述区域内物品摆放关系的区域混合地图；勾绘局部复杂空间的三维栅格地图。然后引入基于QR code技术的自相似二维人工标签——人工物标和人工路标的设计及功能,阐明人工标签在三层环境地图构建中的作用。据此,给出三层环境地图构建模式的具体创建机制及其实现过程：利用双目视觉基于DSmT证据理论构建含体素概念的三维栅格地图；基于SIFT特征匹配算法形成无向加权图,再采用谱聚类算法构建具有房间分割功能的小范围空间语义地图；基于免疫网络算法构建“自主分布式表征”机制下的大范围结构化环境的认知语义地图。最后,初步探讨多个服务机器人同时构建地图时的协作策略及从研究中获得的启示。本文的主要研究内容和结果概括如下：
1.针对单纯依赖本体视觉实现场景识别和理解的复杂性和局限性,提出基于QR code技术的新的室内环境空间认知手段,将移动机器人对环境的理解从几何结构的、模糊的、被动的感知层次,提高到语义的、精确的、主动的认知层次上,形成基于QR code技术的人工标签提供语义信息的地图构建新模式。设计了含外围模式和内部编码两部分的嵌入式人工标签,解决人工标签易被遮挡、QRcode编码远距离难以识别等问题；设计了含物品、房间、走廊等的功能属性和归属关系的QR code编码信息描述规约,为三级室内环境地图的构建提供丰富的语义信息。服务机器人基于高斯模型和椭圆拟合算法搜寻人工标签；基于模糊调整策略和投影透视原理逼近和对准人工标签。
2.针对室内移动机器人的服务任务,提出一种包括全局语义层、区域规划层、局部描述层的三级室内环境模型,使机器人对环境信息的掌握不仅在面向导航的几何结构上,还增加了局部复杂空间的三维栅格信息及描述走廊、房间和物品功能及关联、归属关系的语义信息,并按机器人服务的活动区域和任务执行特点形成各层不同的地图表示形式。首先,对于房间内的小范围环境,机器人依据视觉获得的深度信息及QR code标签提供的物品操作功能信息进行三维栅格地图和物品功能图的构建,形成局域层空间描述；其次,基于贝叶斯估计算法构建区域层的二维栅格地图,同时形成无向加权图,构成区域规划层；最后,基于谱聚类算法构建具有房间分割功能的拓扑地图,结合物品功能图,获得房间功能及房间之间关联关系、物品与房间归属关系等的语义信息,形成全局语义拓扑地图。对于走廊的大范围环境,在获取QR code人工路标提供的语义及引导信息基础上,实现大范围区域的路径导航和认知语义地图的构建。
3.针对室内局部区域的错层结构、立体装修、非结构化家具等复杂环境,构建三维栅格地图。在双目视觉获得深度信息的前提下,基于DSmT证据理论构建双目视觉信息不确定数学模型,并将新、旧信息按比例冲突分配规则进行融合,形成描述体素占有/空闲概率的三维栅格地图。基于特征点关系的稠密匹配获得三维场景的稠密重建,而体素对应匹配点的参数优化能获得最优占空值。在构建三维地图的同时,利用粘贴在大物品上的基于QR code技术的二维人工物标,为环境中的大物品添加语义标签,并基于大物品的尺寸更新对应的体素占空值,形成含大物品功能属性和归属关系描述的三维栅格地图。
4.针对房间内的小范围半未知环境,根据机器人特有的服务任务和人机交互要求,结合大物品上粘贴的基于QR code技术的人工物标,构建半未知环境的以房间为单位的功能语义地图。首先基于谱聚类算法构建具有房间分割功能的拓扑地图。再利用QR code中存储的物品信息,建立物品信息库和物品归属关联图,最终获得包含物品信息描述、房间功能描述及物品-房间归属关系的语义拓扑地图。该地图为室内环境中物品的搜寻、管理和机器人服务提供完备的、拟人化的信息。基于语义地图机器人能理解人的语义命令,生成合理的服务路径,实现功能驱动的导航。
5.针对走廊等大范围结构化半未知环境,提出了基于“自主分布式表征”机制的认知语义地图构建的思路和方法。按照人的路标导航模式,将基于QRcode技术的人工路标分布于环境的关键点处,形成认知导向点。机器人在大范围结构化环境中不断获取认知导向点处的人工路标信息,实现对环境的感知；从感知的信息中提取引导信息,确定目标和路径；对获取信息的逻辑分析,产生运动的行为指令。基于免疫网络算法形成“分布式表征”的环境认知机制,并基于颜色识别和SIFT特征匹配算法实现情景标签的记忆和认知,构建自主分布式的感知-引导-行为网络地图。按认知语义地图实现基于免疫算法的路径重建。
6.针对多机器人协作构建地图的任务,提出了免疫网络探测算法的协作探索策略和基于无线传感网络的机器人融合定位算法。基于观测点融合的改进免疫网络探测算法(INEA),完成多机器人对未知区域的快速、高效的探索任务。该算法在大大减少通信量,又能准确计算各全局观测点花费的情况下,将不同机器人的局部观测点融合在一张地图上,使机器人的协作能力充分发挥。利用T细胞函数修正了免疫网络浓度模型,同时免疫模型参数考虑各观测点的扩散度和探索方向对系统性能的影响。基于RSSI算法对无线传感器网络节点测得的距离数据进行处理,融合基于粒子滤波算法获得的机器人位置数据,实现多机器人的协作定位。
The emergences of service robots in our work and life bring us lots of conveniences more than ever before, such as tea service, garbage removal, freight handling, letter transmission and so on. Therefore, the environmental modeling of robots for service tasks has become the hotspot of current research for map building. The traditional methods of robot map building were focused on the description of spatial structure in spite of considering the functional characteristics of the environment that robots worked in and the complexity of the local space. Besides, they also ignored the semantic information to achieve communion with human. In order to make a service robot be intelligent, we should get inspired from the representation method of the space environment used by people, and study the spatial representation of service robots to understand human language information. Human understanding of the environment is carried out at different levels:the concepts of corridors and rooms are constructed for the large space, and the locations of the object operated by people must be associated with the room; the placement of large items in unit room should provide human the basis of region path planning; the searching and grasping of the objects in local complex space can be achieved by the accomplishment of three-dimensional space model receiving from human's eyes. Therefore, this thesis will focus on the imitating spatial modeling method of the human, aiming to the three-level service task direction for the object management, moving and operations, and building indoor three-level environment maps in unknown environment. According to the three level environment maps of global semantic, regional planning and local description, three level step-up planning system is formulated, which makes the service robot can not only get room segmentation model for unknown environment to determine the accessorial relationship between room and objects, but also complete path planning and navigation in the corridor, the room and so on. At the same time, the three-dimensional map is built around the operated object. Based on the service mission direction, the robot can use "semantic description" to realize the logical reasoning, and determine the "target" and its location. It will lay the foundation for "intelligent" navigation which is driven by the service mission and based on hierarchical environment map modeling mode. As the multi-robots system have many advantages such as being splitted, reconfigurability, fault tolerance and robustness, the study of building map by multi-robots system is being unfolded. In this thesis, on the basis of three-level map building pattern, collaboration strategy and positioning problem of multi-robot system are discussed preliminarily.
The main ideas of this thesis are divided into four steps. Firstly, aim at analyzing the status and problems of the current environment map building, a new three-tier space building pattern is proposed basing on robot service mission direction:the global semantic map reflecting room topology relation; the region hybrid map describing object location relation; three-dimensional grid map drawing local complex space. Then, some two-dimensional artificial tags (artificial object-mark and artificial signpost) based on QR code (Quick Response Code) technology are designed, and the function of artificial labels in the three-level environment map building is also discussed. Accordingly, the specific created mechanism and implementation process of three layer environment model are given:building three dimensional grid map based on DSmT(Dezert-Smarandache Theory) evidence theory which contains voxel concept and uses binocular visual; undirected weighted graph based on SIFT (Scale-Invariant Feature Transform) features match algorithm are formed and small range space semantic map is built basing on spectrum clustering algorithms which has room segmentation function; the cognizing semantic map of large range and structural environment is built basing on immune network algorithm which includes "independent distributed representation" mechanism. Finally, the preliminary study for collaboration strategies of multi-robots is done and inspiration from the research is introduced. The main research contents and results are shown as follows:
1. A new environment cognition method based on QR code technology is proposed to solve the complexity and limitations of visual recognition and scene understanding which relies only on the robot's vision. This method can extend the mobile robot's understanding of the environment from the geometrical structure, vague, passively perceptive level, to semantics, accurate, actively cognitive level, and create a new model of building map with an artificial label providing semantic information. Artificial labels are designed with the external model and internal code to solve the problems such as labels sheltered and identifying QR code difficultly from long-range distance; containing items of QR code information description are designed to provide plentiful semantic information which includes the functional properties and adscription relations of objects, rooms, corridors. Service robot searches artificial labels based on Gaussian model and the elliptical fitting algorithm; approaches and aims at the artificial labels based on fuzzy adjustment algorithm and perspective projection principle.
2. Three-level indoor space maps including global semantic layer, region planning layer and local describing layer are built for robot service mission. Use this space mapping pattern, the robot can not only know the plane structure of the environment for navigation, but also add three-dimensional grid map of local complicated space and semantic information which can describe the function, relationship and ascription of the room and the object. The different maps of every layer are formed according to the service region and task execution characteristics of the robot. Firstly, as the small scope inside rooms, depth information acquired by vision and object function information acquired by QR code label are used to build a three-dimensional grid map and an object function map which describe local space. Then a planar grid map is built basing on Bayesian estimate algorithm, and a non-direction-power-map is formed as well. Therefore, the region planning layer is achieved. Lastly, room-division topology map is built basing on clustering algorithms, semantic information including room functional information, relationship and object attributive relation are obtained, which constitute global semantic topology map. In the large scope as corridors, the navigation and cognizing semantic map building are realized according to the semantic and navigating information achieved from QR code artificial signpost.
3. According to the complexity of indoor environments, such as staggered floor and tridimensional fitment and irregular furniture, a three-dimensional map is built for local space. Under the premise of gaining depth information with binocular vision, the uncertain mathematical model of binocular vision information is structured basing on the DSmT evidence theory, and the new and old information are fused by proportional conflict redistribution rule, then a three-dimensional grid map is formed which describes the occupied/free probability of the voxel. The dense matching based on feature point relations for image must be done to archive dense rebuilding of 3D scene, and the parameter optimization of the matching points in the voxels must also be done to calculate the optimal duty value.While structuring the three-dimensional map, QR code based object marks plastered on large objects are used to give semantic information, and the occupied/free values of the corresponding voxels are updated based on the large object's dimension, then three-dimensional grid semantic map is formed which includes the function property and attributive relation of the large objects.
4. Aim at robot special service tasks and man-machine conversation in the small semi-unknown environment as rooms, a functional semantic map is built using QR code based object mark plastered on large objects. First, a room-function topology map is built basing on the clustering algorithms. Then object information database and adscription relationship map are set up basing on object information stored in the QR code. Finally, a semantic map including object information description, room functional information and attributive relation between room and object is formed, which gives complete and personified information for object location, object management and robot service in indoor environment. The simulation results show that the service robot using semantic map can understand human semantic statement, produce reasonable service path, and achieve function-driven navigation.
5. For large-scale structural and semi-unknown environments such as corridors, the ideas and methods of cognitive semantic map building are proposed basing on the "independent distributed representation". According to the navigation patterns of the human signpost, the artificial signposts based on QR code technology are distributed in the key points of the environment to form cognitive-oriented points. The robot constantly receives the artificial signpost information at cognitive-oriented points in a wide range of structured environment to achieve the perception of the environment; extracts leading information from the perception information to identify goals and paths; logically analyses the obtained information to make motion instructions. Immune network algorithm is used to form the environmental awareness mechanism of "distributed representation", color recognition and SIFT feature matching algorithm are used to achieve the memory and cognition of scenario tag, then the Cognition-Guide-Behavior Map is built as "independent distributed representation". The cognitive semantic map is used to path reconstruction by immune algorithm.
6. For the task of building maps by multi-robots cooperation, the exploration strategy based on immune network exploration algorithm and fusion localization algorithm based on wireless sensing network are brought forward in the thesis. An algorithm named immune network exploration algorithm(INEA) based on observation point fusion is proposed to quickly accomplish m'ulti-robots' exploration task for an unknown environment. In the case of reducing communication greatly and calculating general observation point costs efficiently, local observation points of individual robots fuse on a map, so robots' collaboration ability is incarnated enough. Furthermore, T-cell function is used to update the immune network concentration model, and it is taken into account that system performance is affected by diffuse degree and exploring direction of observation points. Simulation results are provided to validate the efficiency of the complete exploration. The distance measured in WSN is calculated basing on the received signal strength indicators (RSSI), and location data received by particle filter algorithm. They are fused to realize multi-robot collaborative localization.
本文的研究思路分四步展开。首先在总结分析当前环境地图构建的现状和问题基础上,提出基于服务任务导向的三层环境地图构建模式：反映房间拓扑关系的全局语义地图；描述区域内物品摆放关系的区域混合地图；勾绘局部复杂空间的三维栅格地图。然后引入基于QR code技术的自相似二维人工标签——人工物标和人工路标的设计及功能,阐明人工标签在三层环境地图构建中的作用。据此,给出三层环境地图构建模式的具体创建机制及其实现过程：利用双目视觉基于DSmT证据理论构建含体素概念的三维栅格地图；基于SIFT特征匹配算法形成无向加权图,再采用谱聚类算法构建具有房间分割功能的小范围空间语义地图；基于免疫网络算法构建“自主分布式表征”机制下的大范围结构化环境的认知语义地图。最后,初步探讨多个服务机器人同时构建地图时的协作策略及从研究中获得的启示。本文的主要研究内容和结果概括如下：
1.针对单纯依赖本体视觉实现场景识别和理解的复杂性和局限性,提出基于QR code技术的新的室内环境空间认知手段,将移动机器人对环境的理解从几何结构的、模糊的、被动的感知层次,提高到语义的、精确的、主动的认知层次上,形成基于QR code技术的人工标签提供语义信息的地图构建新模式。设计了含外围模式和内部编码两部分的嵌入式人工标签,解决人工标签易被遮挡、QRcode编码远距离难以识别等问题；设计了含物品、房间、走廊等的功能属性和归属关系的QR code编码信息描述规约,为三级室内环境地图的构建提供丰富的语义信息。服务机器人基于高斯模型和椭圆拟合算法搜寻人工标签；基于模糊调整策略和投影透视原理逼近和对准人工标签。
2.针对室内移动机器人的服务任务,提出一种包括全局语义层、区域规划层、局部描述层的三级室内环境模型,使机器人对环境信息的掌握不仅在面向导航的几何结构上,还增加了局部复杂空间的三维栅格信息及描述走廊、房间和物品功能及关联、归属关系的语义信息,并按机器人服务的活动区域和任务执行特点形成各层不同的地图表示形式。首先,对于房间内的小范围环境,机器人依据视觉获得的深度信息及QR code标签提供的物品操作功能信息进行三维栅格地图和物品功能图的构建,形成局域层空间描述；其次,基于贝叶斯估计算法构建区域层的二维栅格地图,同时形成无向加权图,构成区域规划层；最后,基于谱聚类算法构建具有房间分割功能的拓扑地图,结合物品功能图,获得房间功能及房间之间关联关系、物品与房间归属关系等的语义信息,形成全局语义拓扑地图。对于走廊的大范围环境,在获取QR code人工路标提供的语义及引导信息基础上,实现大范围区域的路径导航和认知语义地图的构建。
3.针对室内局部区域的错层结构、立体装修、非结构化家具等复杂环境,构建三维栅格地图。在双目视觉获得深度信息的前提下,基于DSmT证据理论构建双目视觉信息不确定数学模型,并将新、旧信息按比例冲突分配规则进行融合,形成描述体素占有/空闲概率的三维栅格地图。基于特征点关系的稠密匹配获得三维场景的稠密重建,而体素对应匹配点的参数优化能获得最优占空值。在构建三维地图的同时,利用粘贴在大物品上的基于QR code技术的二维人工物标,为环境中的大物品添加语义标签,并基于大物品的尺寸更新对应的体素占空值,形成含大物品功能属性和归属关系描述的三维栅格地图。
4.针对房间内的小范围半未知环境,根据机器人特有的服务任务和人机交互要求,结合大物品上粘贴的基于QR code技术的人工物标,构建半未知环境的以房间为单位的功能语义地图。首先基于谱聚类算法构建具有房间分割功能的拓扑地图。再利用QR code中存储的物品信息,建立物品信息库和物品归属关联图,最终获得包含物品信息描述、房间功能描述及物品-房间归属关系的语义拓扑地图。该地图为室内环境中物品的搜寻、管理和机器人服务提供完备的、拟人化的信息。基于语义地图机器人能理解人的语义命令,生成合理的服务路径,实现功能驱动的导航。
5.针对走廊等大范围结构化半未知环境,提出了基于“自主分布式表征”机制的认知语义地图构建的思路和方法。按照人的路标导航模式,将基于QRcode技术的人工路标分布于环境的关键点处,形成认知导向点。机器人在大范围结构化环境中不断获取认知导向点处的人工路标信息,实现对环境的感知；从感知的信息中提取引导信息,确定目标和路径；对获取信息的逻辑分析,产生运动的行为指令。基于免疫网络算法形成“分布式表征”的环境认知机制,并基于颜色识别和SIFT特征匹配算法实现情景标签的记忆和认知,构建自主分布式的感知-引导-行为网络地图。按认知语义地图实现基于免疫算法的路径重建。
6.针对多机器人协作构建地图的任务,提出了免疫网络探测算法的协作探索策略和基于无线传感网络的机器人融合定位算法。基于观测点融合的改进免疫网络探测算法(INEA),完成多机器人对未知区域的快速、高效的探索任务。该算法在大大减少通信量,又能准确计算各全局观测点花费的情况下,将不同机器人的局部观测点融合在一张地图上,使机器人的协作能力充分发挥。利用T细胞函数修正了免疫网络浓度模型,同时免疫模型参数考虑各观测点的扩散度和探索方向对系统性能的影响。基于RSSI算法对无线传感器网络节点测得的距离数据进行处理,融合基于粒子滤波算法获得的机器人位置数据,实现多机器人的协作定位。
The emergences of service robots in our work and life bring us lots of conveniences more than ever before, such as tea service, garbage removal, freight handling, letter transmission and so on. Therefore, the environmental modeling of robots for service tasks has become the hotspot of current research for map building. The traditional methods of robot map building were focused on the description of spatial structure in spite of considering the functional characteristics of the environment that robots worked in and the complexity of the local space. Besides, they also ignored the semantic information to achieve communion with human. In order to make a service robot be intelligent, we should get inspired from the representation method of the space environment used by people, and study the spatial representation of service robots to understand human language information. Human understanding of the environment is carried out at different levels:the concepts of corridors and rooms are constructed for the large space, and the locations of the object operated by people must be associated with the room; the placement of large items in unit room should provide human the basis of region path planning; the searching and grasping of the objects in local complex space can be achieved by the accomplishment of three-dimensional space model receiving from human's eyes. Therefore, this thesis will focus on the imitating spatial modeling method of the human, aiming to the three-level service task direction for the object management, moving and operations, and building indoor three-level environment maps in unknown environment. According to the three level environment maps of global semantic, regional planning and local description, three level step-up planning system is formulated, which makes the service robot can not only get room segmentation model for unknown environment to determine the accessorial relationship between room and objects, but also complete path planning and navigation in the corridor, the room and so on. At the same time, the three-dimensional map is built around the operated object. Based on the service mission direction, the robot can use "semantic description" to realize the logical reasoning, and determine the "target" and its location. It will lay the foundation for "intelligent" navigation which is driven by the service mission and based on hierarchical environment map modeling mode. As the multi-robots system have many advantages such as being splitted, reconfigurability, fault tolerance and robustness, the study of building map by multi-robots system is being unfolded. In this thesis, on the basis of three-level map building pattern, collaboration strategy and positioning problem of multi-robot system are discussed preliminarily.
The main ideas of this thesis are divided into four steps. Firstly, aim at analyzing the status and problems of the current environment map building, a new three-tier space building pattern is proposed basing on robot service mission direction:the global semantic map reflecting room topology relation; the region hybrid map describing object location relation; three-dimensional grid map drawing local complex space. Then, some two-dimensional artificial tags (artificial object-mark and artificial signpost) based on QR code (Quick Response Code) technology are designed, and the function of artificial labels in the three-level environment map building is also discussed. Accordingly, the specific created mechanism and implementation process of three layer environment model are given:building three dimensional grid map based on DSmT(Dezert-Smarandache Theory) evidence theory which contains voxel concept and uses binocular visual; undirected weighted graph based on SIFT (Scale-Invariant Feature Transform) features match algorithm are formed and small range space semantic map is built basing on spectrum clustering algorithms which has room segmentation function; the cognizing semantic map of large range and structural environment is built basing on immune network algorithm which includes "independent distributed representation" mechanism. Finally, the preliminary study for collaboration strategies of multi-robots is done and inspiration from the research is introduced. The main research contents and results are shown as follows:
1. A new environment cognition method based on QR code technology is proposed to solve the complexity and limitations of visual recognition and scene understanding which relies only on the robot's vision. This method can extend the mobile robot's understanding of the environment from the geometrical structure, vague, passively perceptive level, to semantics, accurate, actively cognitive level, and create a new model of building map with an artificial label providing semantic information. Artificial labels are designed with the external model and internal code to solve the problems such as labels sheltered and identifying QR code difficultly from long-range distance; containing items of QR code information description are designed to provide plentiful semantic information which includes the functional properties and adscription relations of objects, rooms, corridors. Service robot searches artificial labels based on Gaussian model and the elliptical fitting algorithm; approaches and aims at the artificial labels based on fuzzy adjustment algorithm and perspective projection principle.
2. Three-level indoor space maps including global semantic layer, region planning layer and local describing layer are built for robot service mission. Use this space mapping pattern, the robot can not only know the plane structure of the environment for navigation, but also add three-dimensional grid map of local complicated space and semantic information which can describe the function, relationship and ascription of the room and the object. The different maps of every layer are formed according to the service region and task execution characteristics of the robot. Firstly, as the small scope inside rooms, depth information acquired by vision and object function information acquired by QR code label are used to build a three-dimensional grid map and an object function map which describe local space. Then a planar grid map is built basing on Bayesian estimate algorithm, and a non-direction-power-map is formed as well. Therefore, the region planning layer is achieved. Lastly, room-division topology map is built basing on clustering algorithms, semantic information including room functional information, relationship and object attributive relation are obtained, which constitute global semantic topology map. In the large scope as corridors, the navigation and cognizing semantic map building are realized according to the semantic and navigating information achieved from QR code artificial signpost.
3. According to the complexity of indoor environments, such as staggered floor and tridimensional fitment and irregular furniture, a three-dimensional map is built for local space. Under the premise of gaining depth information with binocular vision, the uncertain mathematical model of binocular vision information is structured basing on the DSmT evidence theory, and the new and old information are fused by proportional conflict redistribution rule, then a three-dimensional grid map is formed which describes the occupied/free probability of the voxel. The dense matching based on feature point relations for image must be done to archive dense rebuilding of 3D scene, and the parameter optimization of the matching points in the voxels must also be done to calculate the optimal duty value.While structuring the three-dimensional map, QR code based object marks plastered on large objects are used to give semantic information, and the occupied/free values of the corresponding voxels are updated based on the large object's dimension, then three-dimensional grid semantic map is formed which includes the function property and attributive relation of the large objects.
4. Aim at robot special service tasks and man-machine conversation in the small semi-unknown environment as rooms, a functional semantic map is built using QR code based object mark plastered on large objects. First, a room-function topology map is built basing on the clustering algorithms. Then object information database and adscription relationship map are set up basing on object information stored in the QR code. Finally, a semantic map including object information description, room functional information and attributive relation between room and object is formed, which gives complete and personified information for object location, object management and robot service in indoor environment. The simulation results show that the service robot using semantic map can understand human semantic statement, produce reasonable service path, and achieve function-driven navigation.
5. For large-scale structural and semi-unknown environments such as corridors, the ideas and methods of cognitive semantic map building are proposed basing on the "independent distributed representation". According to the navigation patterns of the human signpost, the artificial signposts based on QR code technology are distributed in the key points of the environment to form cognitive-oriented points. The robot constantly receives the artificial signpost information at cognitive-oriented points in a wide range of structured environment to achieve the perception of the environment; extracts leading information from the perception information to identify goals and paths; logically analyses the obtained information to make motion instructions. Immune network algorithm is used to form the environmental awareness mechanism of "distributed representation", color recognition and SIFT feature matching algorithm are used to achieve the memory and cognition of scenario tag, then the Cognition-Guide-Behavior Map is built as "independent distributed representation". The cognitive semantic map is used to path reconstruction by immune algorithm.
6. For the task of building maps by multi-robots cooperation, the exploration strategy based on immune network exploration algorithm and fusion localization algorithm based on wireless sensing network are brought forward in the thesis. An algorithm named immune network exploration algorithm(INEA) based on observation point fusion is proposed to quickly accomplish m'ulti-robots' exploration task for an unknown environment. In the case of reducing communication greatly and calculating general observation point costs efficiently, local observation points of individual robots fuse on a map, so robots' collaboration ability is incarnated enough. Furthermore, T-cell function is used to update the immune network concentration model, and it is taken into account that system performance is affected by diffuse degree and exploring direction of observation points. Simulation results are provided to validate the efficiency of the complete exploration. The distance measured in WSN is calculated basing on the received signal strength indicators (RSSI), and location data received by particle filter algorithm. They are fused to realize multi-robot collaborative localization.
引文
[l]服务机器人的现状及发展趋势[J], http://www.docin.com/p-47442726.html#documentinfo
[2]蔡自兴,何汉根,陈虹,未知环境中移动机器人导航控制理论与方法[M],科学出版社,2009.
[3]Leonard J, Durrant-Whyte H F, Mobile Robot Localization by Tracking Geometric Beacons[J], IEEE Transactions on Robotics and Automation,7(3),1991,376-382.
[4]Y.Uny Cao, Alex S. Fukunaga, Andrew B. Kahng, Cooperative mobile robotics:antecedents and directions [J], Autonomous Robots,1997,4(1):7-27.
[5]Aarne Halme, The Concept of Robot Society and its Utilization[A], Proceedings of the 1993 IEEE International Workshop on Advanced Robotics Tsukuba. Japan November 8-9, 1993:123-134.
[6]A. Tapus, R. Siegwart, Incremental robot mapping with fingerprints of places[A], in:Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS,2005, pp. 2429-2434.
[7]S. Thrun, Learning metric-topological maps for indoor mobile robot navigation[J], Artificial Intelligence,1998,99(1):21-31.
[8]B. Kuipers, Y.-T. Byun, A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations [J], Robotics & Autonomous Systems,1991,12(8): 47-63.
[9]Quijano, Humberto J.; Garrido, Leonardo, Improving Cooperative Robot Exploration Using an Hexagonal World Representation[A], Electronics, Robotics and Automotive Mechanics Conference,2007 CERMA,25-28 Sept.2007:450-455.
[10]Sen Wang; Yang Wang; Miao Jin; Xianfeng Gu, Samaras, D,3D Surface Matching and Recognition Using Conformal Geometry[A],2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2,2006:2453-2460.
[11]Motard, E.; Raducanu, B.; Cadenat, V.; Vitria, J.; Incremental On-Line Topological Map Learning for A Visual Homing Application[A],2007 IEEE International Conference on Robotics and Automation,10-14 April,2007:2049-2054.
[12]H. Choset and K. Nagatani. Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization[J], IEEE Transactions on Robotics and Automation,2001,17(2):79-95.
[13]Banjanovic-Mehmedovic, L., Petrovic. I.; Ivanjko, E., Mobile Robot Localization Using Local Occupancy Grid Maps Transformations[A],12th International Power Electronics and Motion Control Conference, Aug.2006:1307-1312
[14]Boada, B.L., Castejon, C., Moreno, L., Topo-geometric modeling and localization in indoor environments[A], IEEE 2002 28th Annual Conference of the Industrial Electronics Society, Volume 4,5-8 Nov.2002:2587-2592.
[15]P. Beeson, N.K. Jong, B. Kuipers, Towards autonomous topological place detection using the extended voronoi graph[A], in:Proc. of the IEEE Int. Conf. on Robotics & Automation, 2005:1877-1983.
[16]A. Tapus, Topological SLAM:simultaneous localization and mapping with fingerprints of places [D], Swiss Federal Institute of Technology, Lausanne,2005.
[17]Byung Jae Park; Se-Jin Lee, Jong-Hwan Lim, Dong-Woo Cho, Angular Uncertainty Reduction of Sonar Range Data for a Grid-based Map Building[A],2006 International Joint Conference on SICE-ICASE, Oct.2006:2010-2014.
[18]Leeke, S., Maharatna, L., A low-power geometric mapping co-processor for high-speed graphics application[A],2006 IEEE International Symposium on Circuits and Systems, 21-24 May,2006:328-331.
[19]吴培良,孔令富,赵逢达,一种服务机器人家庭全息地图构建方法研究[J],计算机应用研究,2010(3)：982-986
[20]蔡自兴,邹小兵,移动机器人环境认知理论与技术的研究[J],机器人,2004(1)：87-92.
[21]Sebastian T, Wolfram B, Dieter F. A real-time algorithm for mobile robot mapping with application to multi-robot and 3D mapping [A], the IEEE int. conf. on robotics and automation.San Francisco,2000:1543-1551.
[22]Andrew J D, Nobuyuki K.3D simultaneous localization and map building using active vision for a robot moving on undulating terrain[A]. the IEEE computer society conference on computer vision and pattern recognition. Hawaii,2001:2811-2819.
[23]Se S, Lowe D, Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks [J]. Robotics research,2002,21(8):735-738.
[24]S. Thrun, Learning metric-topological maps for indoor mobile robot navigation[J], Artificial Intelligence,1998,99(1):21-71.
[25]N. Tomatis, I. Nourbakhsh and R. Siegwart, Hybrid Simultaneous Localization and Map Building:A Natural Integration of Topological and Metric[J], Robotics and Autonomous Systems,2003,44(7):3-14.
[26]A. Martinelli, A. Tapus, K.O. Arras and R. Siegwart, Multi-resolution SLAM for Real World Navigation[A], In Proceedings of the 11th International Symposium of Robotics Research, Siena, Italy,2003:449-456.
[27]王珂,王伟,庄严,孙传昱,基于几何—拓扑广域三维地图和全向视觉的移动机器人自定位,自动化学报[J],2008,34(11)：1369-1378.
[28]Sangwoo Jo, Qonita M. Shahab, Yong-Moo Kwon, Sang Chul Ahn, Indoor Modeling for Interactive Robot Service[A],2006 International Joint Conference on SICE-ICASE, Bexco, Busan, Korea,2006:3531-3536.
[29]梁志伟,马旭东,戴先中,房芳,基于分布式感知的移动机器人同时定位与地图创建[J],机器人,2009,31(1)：33-39.
[30]B. Peter, et al.,3D Modeling of Indoor Environments by a Mobile Robot with a Laser Scanner and Panoramic Camera [A], IEEE International Conference on Intelligent Robots and Systems (IROS 2004),2004, Vol.4:3430-3435.
[31]C. Galindo, A. Saffiotti, S. Coradeschi, P. Buschka, J.A. Fernandez-Madrigal and J. Gonz adlez, Multi-Hierarchical Semantic Maps for Mobile Robotics[A], Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), Edmonton, CA,2005:3492-3497.
[32]B. Kuipers, The Spatial Semantic Hierarchy[J], Artificial Intelligence,2000,119(5): 191-233.
[33]S. Vasudevan, S. Gachter, V. Nguyen, R. Siegwart, Cognitive maps for mobile robots_and object based approach[J], Robotics and Autonomous Systems,2007,55(3):359-371.
[34]G. Kruijff, H. Zender, P. Jensfelt, H. Christensen, Situated dialogue and spatial organization:What, where... and why?[J], Advanced Robotic System,2007,4 (2): 125-138.
[35]Arleo A, Gerstner W. Spatial cognition and neuro mimetic navigation:a model of hippocampal place cell activity [J]. Biological Cybernetics.2000,83 (3):287-299.
[361 E.C. Tolman, Cognitive maps in rats and men[J]. Psychological Review,1948,55(3): 189-208.
[37]B. J. Kuipers, The cognitive map:Could it have been any other way?[R], Spatial Orientation:Theory, Research, and Application. New York:Plenum Press,1983:345-359.
[38]W.K. Yeap and M.E. Jefferies, On early cognitive mapping[J], Spatial Cognition and Computation 2001,2(2):85-116.
[39]C. Stachniss,o. Marti nez-Mozos, A. Rottmann, and W. Burgard, Semantic Labeling of Places[A], In Proc. of the Int. Symposium of Robotics Research (ISRR).San Francisco, CA, USA,2005:89-95.
[40]A. Torralba, K. P. Murphy, W. T. Freeman and M. A. Rubin, Contextbased vision system for place and object recognition[A], Proceedings of the IEEE International Conference on Computer Vision, vol.1, Nice, France:191-203.
[41]Mallot H A. Spatial cognition, behavioral competences, neural mechanisms, and evolutionary scaling[J]. Kognition swissenschaft,1999,8(1):40-48.
[42]O. Mozos, P. Jensfelt, H. Zender, M. Kruijff, W. Burgard, From labels to semantics:An integrated system for conceptual spatial representations of indoor environments for mobile robots[R], in:ICRA Workshop:Semantic Information in Robotics,2007:395-403.
[43]H. Zender, P. Jensfelt, O. Martinez-Mozos, G.-J. Kruijff, W. Burgard, An integrated robotic system for spatial understanding and situated interaction in indoor environments[A],2007 AAAI,2007:1584-1589.
[44]P. Beeson, M. MacMahon, J. Modayil, A. Murarka, B. Kuipers, B. Stankiewicz, Integrating Multiple Representatios of Spatial Knowledge for Mapping, Navigation, and Communication[A], Interaction Challenges for Intelligent Assistants, AAAI Spring Symposium Series,2007:513-520.
[45]H. Zender, Learning spatial organization through situated dialogue[D], Saarland University, Dept. of Computational Linguistics, Saarbrcken, Germany,2006.
[46]C. Galindo, A. Saffiotti, S. Coradeschi, P. Buschka, J. Fernandez-Madrigal, J. Gonzalez, Multi-hierarchical semantic maps for mobile robotics[A], in:Proc of the IEEE/RSJ Int Conf on Intelligent Robots and Systems, IROS, Edmonton, CA,2005:3492-3497.
[47]D. Meger, P. Forssen, K. Lai, S. Helmer, S. McCann, T. Southey, M. Baumann, J. Little, D. Lowe, B. Dow, Curious george:An attentive semantic robot[R], in:IROS Workshop: From Sensors to Human Spatial Concepts,2007:390-404.
[48]A. Ranganathan, F. Dellaert, Semantic modeling of places using objects[A], in:Robotics: Science and Systems Conf.,2007:45-53.
[49]S. Vasudevan, S. Gachter, V.T. Nguyen, R. Siegwart, Cognitive maps for mobile robots— — an object based approach[J], Robotics and Autonomous Systems,2007,55 (5): 359-371.
[50]D. Anguelov, R. Biswas, D. Koller, B. Limketkai, S. Sanner, S. Thrun, Learning hierarchical object maps of non-stationary environments with mobile robots[A], in:Proc. of the Conf. on Uncertainty in Artificial Intelligence, UAI,2002:10-17.
[51]D. Anguelov, D. Koller, E. Parker, S. Thrun, Detecting and modeling doors with mobile robots[A], in:Proc. of the IEEE Int. Conf. on Robotics & Automation,2004:3777-3784.
[52]B. Limketkai, L. Liao, D. Fox, Relational object maps for mobile robots[A], in:Proc. of the Int. Conf. on Artificial Intelligence, IJCAI, Edinburgh, Scotland,2005:1471-1476.
[53]D.G. Lowe, Distinctive image features from scale-invariant keypoints[J], Computer Vision,2004,60(2):91-110.
[54]Shrihari Vasudevan, Viet Nguyen & Roland Siegwart, Towards a Cognitive Probabilistic Representation of Space for Mobile Robots[A],2006 IEEE International Conference on Information Acquisition August,2006, Weihai, Shandong, China:353-360.
[55]D. Anguelov, D. Koller, E. Parker, S. Thrun, Detecting and Modeling Doors with Mobile Robots[A], Proceedings of the International Conference on Robotics and Automation (ICRA),2004:636-642.
[56]D. Kortenkamp, L. D. Baker, and T. Weymouth, Using Gateways to Build a Route Map[A], Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems,1992:93-101.
[57]Claire D'Este, Claude Sammut, Learning and generalising semantic knowledge from object scenes[J], Robotics and Autonomous Systems,2008 (56:891-990.
[58]吴皓,田国会,黄彬,未知环境探测的多机器人协作策略研究[J],山东大学学报,2008.38(4)：27-31
[59]张洪峰,王硕,谭民,王龙,基于动态分区方法的多机器人协作地图构建[J],机器人,2003,25(2)：156-162.
[60]Chan Sze Kong. New Ai Peng, Ioannis Rekleitis, Distributed Coverage with Multi-Robot System[A], Proceedings of the 2006 IEEE International Conference on Robotics and Automation Orlando, Florida:2423-2430.
[61]Yamauchi B, Frontier-based exploration using multiple robots[A], Second International Conference on autonomous Agents,1998:47-53.
[62]Burgard W, Moors M, Fox D, et al, Collaborative multi robot exploration[A]. IEEE Int Conf on Robotics and Automation ICRA, San Francisco IEEE,2000:476-481.
[63]Ioannis R, Vicent L S, Ai PN, et al. Limited communication,multi-robot team based coverage[C]. Proceedings of the IEEE Intl. Conference on Robotics and Automation,2004: 3462-3467.
[64]Oriolo G, Ulivi G. Real-time map building and navigation for autonomous robots in unknown environments[J]. IEEE Trans Systems, Man, and Cybernetics,1998,28(3): 316-332.
[65]R.Grabowski,L.Navarro-Serment,C.Paredis,P.Khosla,Heterogeneous teams of modular robots for mapping and exploration[J], Autonomous Robots,2000,8(3):293-308.
[66]Zlot R, Stentz A, Dias M B, et al. Multi robot exploration controlled by a market economy[A]. Proc of the IEEE Int Conf on Robotics and Automation(ICRA)[C]. Washington IEEE Press,2002:3016-3023.
[67]高云园,韦巍,基于免疫机理的多机器人未知环境完全探测研究[J],模式识别与人工智能,2007.4：191-196.
[68]HowardA, MataricM.J. and SukhatmeG.S., Localization for Mobil Robot Teams Using Maximum Likelihood Estimation[C], International Conference on Intelligent Robot and Systems (IROS02), Volume:3,30 and Sept.2002:2849-2854.
[69]HowardA, Maja J, Sukhatme G. Localization for mobile robot teams:a distributed MLE approach[J]. Experimental Robotics Ⅷ, Bruno Siciliano and Paulo Dario(eds) Springer-Verlag,2003:146-155.
[70]S.I.Roumeliotis and G.A.Bekey,2002, Distributed Multi robot Localization[J], IEEE Transaction on Robotics and Automation,2002,18(5):533-540.
[71]Roumeliotis S, Bekey G. Collective localization:a distributed kalman filter approach [A]. Proceedings of the IEEE International Conference on Robotics and Automation [C], San Francisco, USA,2000,3:2958-2965.
[72]Andrew Howard, Multi-Robot Mapping using Manifold Representations[A], proceedings of the 2004 IEEE International Conference on Robot and Automation New Orleans, April,2004:49-57.
[73]Fox D, Burgard W, Kruppa Hetal. A probabilistic approach to collaborative multi -robot localization[J]. Autonomous Robots on Heterogeneous Multi-Robot Systems,2000,8(3): 325-344.
[74]王玲,邵金鑫,万建伟,刘云辉,多机器人定位中基于熵的分布式观测量选择方法[J],电子学报,2007.]：333-336.
[75]刘俊承,原魁,邹伟,朱海兵,基于特征粒子的Monte Carlo自定位方法,机器人,2006.1：30-35.
[76]Mourikis, A. I.; Roumeliotis, S. I.; Burdick, J. W.; SC-KF Mobile Robot Localization:A Stochastic Cloning Kalman Filter for Processing Relative-State Measurements[J], IEEE Transactions on Robotics and Automation,2007,23(4):717-730.
[77]姜健,赵杰,李力坤,面向群智能机器人系统的声音协作定向[J],自动化学报,2007.4：385-391.
[78]Terence Chung Hsin Sit, ZhengLiu, Marcelo H. Ang Jr., Winston Khoon Guan Seah, Multi-robot mobility enhanced hop-count based localization in ad hoc networks[J], Robotics and Autonomous Systems,2007(55):244-252.
[79]Sun Peigang, Zhao Hai, Luo Dingding, Zhang Xiaodan, Yin Zhenyu,Research on RSSI-based location in smart space[J], The transaction of electronics,2007.7:1240-1244.
[80]FAN Le-hao, QIU Xiao-hui, TANG Yi-bin, Discussion on self-location algorithm for wireless sensor networks[J], Telecommunications for Electric Power System,2007 (10): 19-23.
[81]Robert B. Johnston, Alvin Khin Choy Yap, Electronic Data Interchange using Two Dimensional Bar Code [D]. Department of Business Systems, Monash University, Australia,1998:235-242.
[82]中国标准出版社.条码国家标准汇编[M].北京：中国标准出版社,2004.
[83]G. Grisetti. C. Stachniss, and W. Burgard. Improved techniques for grid mapping with Rao-Blackwellized particle filters [J]. IEEE Trans. Robot.,2007,23(1):34-46.
[84]C. Stachniss, W. Burgard. Mapping and exploration with mobile robots using coverage maps[A]. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'2003),2003:467-472.
[85]Van Zwynsvoorde D, Simeon T. Alami R, Incremental topological modeling using local voronoi-like graphs [A], Proceeding of the 2000 IEEE/RSJ International Conference on Intelligent Robot s and Systems,2000:897-902.
|86] P. Beeson, N.K. Jong, B. Kuipers, Towards autonomous topological place detection using the extended voronoi graph [A]. Proceedings of the IEEE International Conference on Robotics and Automation,2005:4373-4379.
[87]J.L.Blanco, J.Gonz'alez, J.A.Fern'and ez-Madrigal, Subjective local maps for hybrid metric-topological SLAM[J], Robotics and Autonomous Systems,2009,57(3):64-74.
[88]Jose-Luis Blanco, Juan-Antonio Fernandez-Madrigal, and Javier Gonzalez, Toward a Unified Bayesian Approach to Hybrid Metric-Topological SLAM[J], IEEE Transaction on Robotics,2008,24(2):1824-1831.
[89]B. Krose, O. Vlassis, R. Bunschoten, Y. Motomura, A probabilistic model for appearance-based robot localization[J], Image and Vision Computing,2001,19(6): 381-391.
[901王珂,王伟,庄严,孙传昱,基于几何—拓扑广域三维地图和全向视觉的移动机器人自定位[J],自动化学报,2008,34(11)：1369-1378.
[91]Sangwoo Jo, Qonita M. Shahab, Yong-Moo Kwon, Sang Chul Ahn, Indoor Modeling for Interactive Robot Service[A],2006 International Joint Conference on SICE-ICASE, Bexco, Busan, Korea,2006,10:3531-3536.
[92]梁志伟,马旭东,戴先中,房芳,基于分布式感知的移动机器人同时定位与地图创建[J],机器人,2009,31(1)：33-39.
[93]B. Peter, et al.,3D Modeling of Indoor Environments by a Mobile Robot with a Laser Scanner and Panoramic Camera [A], IEEE International Conference on Intelligent Robots and Systems (IROS 2004),2004, Vol.4:3430-3435.
[94]Andreas Nuchter, Joachim Hertzberg, Towards semantic maps for mobile robots[J], Robotics and Autonomous Systems 2008,56(8):915_926.
[95]Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, et al, Towards 3D Point cloud based object maps for household environments[J], Robotics and Autonomous Systems 2008,56(8):927-941.
[96]D. Hanel, et al., Learning Compact 3D Models of Indoor and Outdoor Environments with a Mobile Robot [J], Robotics and Autonomous Systems,2003,44(1):15-27.
[97]Juan Mabuel Saez, Francisco Escolano, A Global 3D Map-Building Approach Using Stereo Vision[A]. Proceedings of the 2004 IEEE international Conference on Robotics &Automation New Orleans,2004, Vol.2:1197-1202.
[98]孙珺,基于地面移动机器人感知信息的三维地图构建[D],南京理工大学,2005.
[99]D. Scharstein, A.J. Briggs. Real-time recognition of self-similar landmarks[J]. Image and Vision Computing,2001,19(11):763-772.
[100]Craig Becker, Joaquin Salas, Kentaro Tokusei, Reliable Navigation Using Landmarks[A]. IEEE International Conference on Robotics and Automation, Montreal,1995:401-406.
[101]Yue Liu Mingjun Liu, Automatic Recognition Algorithm of Quick Response Code Based on Embedded System[A], Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06),2006:201-207.
[102]赵守鹏,基于服务机器人系统的智能空间关键技术研究与设计[D],山东大学,2007.
[103]周祥,孔晓东,曾贵华,一种新的基于Hough变换的椭圆轮廓检测方法[J],计算机工程,2007,8：166-168.
[104]David G. L Distinctive image features from scale-invariant key points[J], Computer Vision,2004,60(2):91-110.
[105]姜海涛,田国会,薛英花,基于新型人工地标的机器人主动控制[J],山东大学学报(工学版),2011,41(2)：236-240.
[106]Beis J S. Lowe D G, Shape indexing using approximate nearest-neighbor search in high-dimensional spaces [C], Proceedings of the IEEE 1997 Computer Society Conference on Computer Vision and Pattern Recognition,1997:1000-1006.
[107]Scharstein D, Szeliski R. y, A Taxonomy and Evaluation of Dense Two-frame Stereo Correspondence Algorithms[J], Computer Vision,2002,47(1):7-42.
[108]Kang-Hyun Kim, Heesung Jun. Realistic 3D Reconstruction From An Image Sequence[A]. Proceedings of the 7th Koren-Russio International Symposium, KORUS 2003:563-570.
[109]P.H.S. Torr, A.W. Fitzgibbon. Invariant Fitting of Two View Geometry[J]. IEEE Transactions On Pattern Analysis And Machine Intelligence,2004,26(5):648-650.
[110]卜范骞,基于智能空间的服务机器人定位与构图技术研究[D],山东大学,2008.
[111]Rui Rocha, Jorge Dias. Adriano Carvalho, Cooperative multi-robot systems:A Study of Vision-Based 3-D Mapping Using Information Theory[J]. Robotics and Autonomous System,2005(53):282-311.
[112]Mikolajczyk K. and Schmid C, An affine invariant interest point detector[A], In European Conference on Computer Vision (ECCV), Copenhagen, Denmark,2002:128-142.
[113]吴福朝.计算机视觉中的数学方法[M]，北京：科学出版社,2008.
[114]谷永发,三维场景重建的理论研究和工程实践[D],西安电子科技大学,2009.
[115]K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors[A]. In International Conference on Computer Vision & Pattern Recognition.2003; volume 2: 257-263.
[116]G. Shafer, A mathematical theory of evidence. Princeton[D], Princeton University,1974.
[117]J. Dezert, F. Smarandache. On the generation of hyper-power sets for the DSmT[A], Proceedings of The 6th Int. Conf. Information Fusion. Australia, Cairns, Queensland.8-11, 2003:1118-1125.
[1181李新德,多源不完善信息融合方法及其应用研究[D].华中科技大学,2007
[119]J. Pearl. Reasoning with belief functions:an analysis of compatibility[J], Approximate Reasoning,1990,4:363-389.
[120]C. K. Murphy. Combining belief functions when evidence conflicts[J]. Decision Support Systems,2000,29:1-9.
[121]杨锦园,黄心汉,李鹏,基于DSmT的移动机器人地图构建及传感器管理[J],计算机科学,2010,27(4)：227-233.
[122]李新德,黄心汉,王敏,基于经典DSmT的Sonar栅格地图创建[J],计算机应用研究,2007,24(3)：209-213.
[123]F.Smarandache, J.Dezert. Advances and Applications of DSmT for information Fusion[F]. Vol.2. Rehoboth, USA:American Research Press,2006:450-469.
[124]Andreas Nuchter, Joachim Hertzberg, Towards semantic maps for mobile robots[J], Robotics and Autonomous System,2008,56(11):915-926.
[125]Wolf, D.F.; Sukhatme, G.S., Semantic Mapping Using Mobile Robots[J], IEEE Transactions on robotics,2008,24(2):245-258.
[126]Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, Mihai Dolha, Michael Beetz, Towards 3D Point cloud based object maps for household environments[J], Robotics and Autonomous Systems,2008,56(11):927-941.
[127]赵守鹏,田国会,李晓磊,基于单个人工地标的机器人自主定位[J],山东大学学报(工 学版),2007,37(4)：39-45.
[128]穴洪涛,田国会,李晓磊,路飞,QR Code在多种类物品识别与操作中的应用[J],山东大学学报(工学版),2007,37(6)：25-31.
[129]A. Ng, M. Jordan, Y. Weiss, on spectral clustering:Analysis and an algorithm[A], in:Proc. Advances in Neural Information Proceeding Systems,2001:345-352.
[130]Ogawa, Y., Shimada, N., Shirai, Y., Environmental mapping for mobile robot by tracking SIFT feature Points using trinocular vision[A], Annual Conference on SICE, Kagawa University, Japan,2007:1996-2001.
[131]D. Scharstein, A.J. Briggs. Real-time recognition of self-similar landmarks[J]. Image and Vision Computing,2001(11):763-772.
[132]SI Bing-yu, Research on the problem concerning visual artificial landmark based navigation of autonomous mobile robot[D], ShenYang:Dongbei University,2003.
[133]Oscar Martinez Mozos, Rudolph Triebel, Patric Jensfelt, Axel Rottmann, Wolfram Burgard, Supervised semantic labeling of places using information extracted from sensor data[J], Robotics and Autonomous Systems,2007(5):391-402.
[134]DissanayakeM W M G, Newman P'Clark S, et al. A solution to the simultaneous localization and map building(SLAM)problem[J]. IEEE Transactions on Robotics and Automation,2001,17(3):229-241.
[135]郭剑辉,赵春霞,石杏喜,一种改进的联合相容SLAM数据关联方法[J],仪器仪表学报,2008,29(11)：2260-2265.
[136]Davey S J. Simultaneous localization and map building using the probabilistic multi— hypothesis tracker[J]. IEEE Transactions on Robotics,2007,23(2):271-280.
[137]周武,赵春霞,SLAM问题的一种优化数据关联算法[J],机器人,2009(5)：216-223.
[138]郑宏,王景川,陈卫东,基于地图的移动机器人自定位与导航系统[J],机器人,2009,29(4)：397-402.
[139]韩金华,王立权,韩宗真,孟庆鑫,裘运根,护士助手机器人在结构化环境中的导航系统研究[J],哈尔滨工业大学学报,2007,39(7)：1166-1171.
[140]王珂,王伟,庄严,孙传昱,基于几何-拓扑广域三维地图和全向视觉的移动机器人自定位[J],自动化学报,2008,34(]1)：1369-1378.
[141]石朝侠,洪炳镕,周彤,王燕清,大规模环境下的拓扑地图创建与导航,机器人,2007,29(5)：433-439.
[142]LiuJuan, CaiZixing, TuChunming. A Connectionist Approach for Cognitive Map Learning and Navigation Based on Spatio-Temporal Experiences[J], Control Theory and Applications,2003,20(2):161-167.
[143]S. Vasudevan and R. Siegwart, A Bayesian approach to Conceptualization and Place Classification:Incorporating Spatial Relationships (distances) to Infer[R], Proceedings of the IEEE IROS, FS2HSC.2007:55-68.
[144]S. Vasudevan. V. Nguyen, and R. Siegwart, Cognitive Maps for Mobile Robots-An Object based Approach[A], Proceedings of the IROS.2006:7-12.
[145]Chuho Yi.ⅡHong Suh, Gi Hyun Lim, and Byung-Uk Choi, Bayesian Robot Localization using Spatial Object Contexts[A], The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems October 11-15,2009 St. Louis. USA:3467-3474.
1146]司秉玉.基于可视人工路标的自主机器人导航若干问题研究[D]，东北大学，2003.
[147]张明状,庄严,王伟,顾明伟.移动机器人及时环境探索与地图库构建[J],东南大学学报(自然科学版),2009(39)：111-115.
[148]Varela F J.&Coutinho A.& Dupire E.&Vaz, Cognitive Networks:Immune, Neural and Otherwise[J]. Theoretical Immunology,1988:359-375.
[149]Varela F J.&Coutinho A.Second generation immune networks[J], Imm. Today, 1991,12(5):159-166.
[150]吕岗,陈小平,谭得健.免疫算法抗体浓度调节定义的改进[J].数据采集与处理,2003,18(1)：44-48.
[151]Burgard W, Moors M, Fox D, et al, Collaborative multi robot exploration[A]. IEEE Int Conf on Robotics and Automation ICRA, San Francisco IEEE,2000:476-481.
[152]Ioannis R, Vicent L S, Ai PN, et al. Limited communication,multi-robot team based coverage[C]. Proceedings of the IEEE Intl. Conference on Robotics and Automation,2004: 3462-3467.
[153]Zlot R, Stentz A, Dias M B, et al. Multi robot exploration controlled by a market economy[A]. Proc of the IEEE Int Conf on Robotics and Automation(ICRA)[C]. Washington IEEE Press,2002:3016-3023.
[154]Alejandro R.Mosteo, Luis Montano, Comparative experiments on optimization criteria and algorithms for auction based multi-robot task allocation[A],2007 IEEE International Conference on Robotics and Automation, Roma,Italy,2007:3345-3350.
[155]张飞,陈卫东,席裕庚,多机器人协作探索的改进市场法[J],控制与决策,2005,20(5)：516-524.
[156]高云园,韦巍,基于免疫机理的多机器人未知环境完全探测研究[J],模式识别与人工智能,2007.4：191-196.
[157]WeihuaSheng, QingyanYang, JindongTan, NingXi, Distributed multirobot coordination in area exploration [J], Robotics and Autonomous Systems 54(2006):945-955.
[158]Hyun-Deok Kang,Kang-Hyun Jo. Self-localization of mobile robot using Omni-direcional vision[C]. Proceedings of the 7th Korea-Russia International Symposium on Science and Technology, Korea, vol.2, No.28,2003, pp.86-92.
[159]S.Thrun, Y.Liu, D.Koller, A.Y.Ng, Z.Ghahramani, H.Durrant Whyte, Simultaneous localization and mapping with sparse extended information filters[J], Robotics Research, 2004(23):693-716.
[160]Elfes A, Occupancy grids:A stochastic spatial representation for active robot perception[A], In Proc.6th Conference on Uncertainty in AI.1990:60-70.
[161]Yamauchi B, Frontier-based exploration using multiple robots[C], Second International Conference on autonomous Agents,1998:47-53.
[162]N.K. Jerne, Idiotopic Network and Other Preconceived Ideas[J], Immunological Rev., 1984,79:5-24.
[163]Jin-Hyung Jun; Dong-Wook Lee; Kwee-Bo Sim; Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system [A], IEEE SMC'99 Conference Proceedings,1999:614-619.
[164]周光明,贾梦雷,陈宗海,移动机器人未知环境自主探测的一种高效方法[J],上海交通大学学报,2005,39(6)：936-940.
[165]D.Fox, W.Burgard, H.Kruppa, S.Thrun,2000, A Probabilistic Approach to Collaborative Multi-Robot Localization [J], Autonomous Robots,2000,8:325-344.
[166]Juncheng Liu, Kui Yuan, Wei Zou, Qinmin Yang, Monte Carlo Multi-Robot Localization Based on Grid Cells and Characteristic Particles[C], Proceedings of the 2005IEEE/ASME International Conference on Advanced Intelligent Mechatronics, California, USA,24-28 July,2005:510-515.
[167]HowardA, MataricM.J. and SukhatmeG.S., Localization for Mobil Robot Teams Using Maximum Likelihood Estimation[C], International Conference on Intelligent Robot and Systems (IROS02), Volume:3,30 and Sept.-5Oct.2002:2849-2854.
[168]H.Zhao and R.Shibasaki, A novel system for tracking pedestrians using multiple single-row laser-range scanners [J]. IEEE Trans. Syst., Man, Cybern.A,2005,35(2): 283-291.
[169]Terence Chung Hsin Sit, ZhengLiu, Marcelo H. Ang Jr., Winston Khoon Guan Seah, Multi-robot mobility enhanced hop-count based localization in ad hoc networks[J], Robotics and Autonomous Systems,2007,55:244-252.
[170]Sun Peigang, Zhao Hai, Luo Dingding, Zhang Xiaodan, Yin Zhenyu, Research on RSSI-based location in smart space[J], The transaction of electronics,2007.7:1240-1244.
[171]FAN Le-hao, QIU Xiao-hui, TANG Yi-bin, Discussion on self-location algorithm for wireless sensor networks[J], Telecommunications for Electric Power System,2007(10): 19-23.
[172]Erin-Ee-Lin Lau, Wan-Young Chung,Enhanced RSSI-based Real-time User Location Tracking System for Indoor and Outdoor Environments[C],2007 International Conference on Convergence Information Technology,2007:1213-1219.
[173]Ioannis M. Rekleitis, Gregory Dudek, Evangelos E.Milios, Multi-robot cooperative localization:a study of trade-offs between efficiency and accuracy[C], Proceedings of the 2002 IEEE Conference on Intelligent Robots and Systems, Switzerland,2002:2690-2695.
[2]蔡自兴,何汉根,陈虹,未知环境中移动机器人导航控制理论与方法[M],科学出版社,2009.
[3]Leonard J, Durrant-Whyte H F, Mobile Robot Localization by Tracking Geometric Beacons[J], IEEE Transactions on Robotics and Automation,7(3),1991,376-382.
[4]Y.Uny Cao, Alex S. Fukunaga, Andrew B. Kahng, Cooperative mobile robotics:antecedents and directions [J], Autonomous Robots,1997,4(1):7-27.
[5]Aarne Halme, The Concept of Robot Society and its Utilization[A], Proceedings of the 1993 IEEE International Workshop on Advanced Robotics Tsukuba. Japan November 8-9, 1993:123-134.
[6]A. Tapus, R. Siegwart, Incremental robot mapping with fingerprints of places[A], in:Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS,2005, pp. 2429-2434.
[7]S. Thrun, Learning metric-topological maps for indoor mobile robot navigation[J], Artificial Intelligence,1998,99(1):21-31.
[8]B. Kuipers, Y.-T. Byun, A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations [J], Robotics & Autonomous Systems,1991,12(8): 47-63.
[9]Quijano, Humberto J.; Garrido, Leonardo, Improving Cooperative Robot Exploration Using an Hexagonal World Representation[A], Electronics, Robotics and Automotive Mechanics Conference,2007 CERMA,25-28 Sept.2007:450-455.
[10]Sen Wang; Yang Wang; Miao Jin; Xianfeng Gu, Samaras, D,3D Surface Matching and Recognition Using Conformal Geometry[A],2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2,2006:2453-2460.
[11]Motard, E.; Raducanu, B.; Cadenat, V.; Vitria, J.; Incremental On-Line Topological Map Learning for A Visual Homing Application[A],2007 IEEE International Conference on Robotics and Automation,10-14 April,2007:2049-2054.
[12]H. Choset and K. Nagatani. Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization[J], IEEE Transactions on Robotics and Automation,2001,17(2):79-95.
[13]Banjanovic-Mehmedovic, L., Petrovic. I.; Ivanjko, E., Mobile Robot Localization Using Local Occupancy Grid Maps Transformations[A],12th International Power Electronics and Motion Control Conference, Aug.2006:1307-1312
[14]Boada, B.L., Castejon, C., Moreno, L., Topo-geometric modeling and localization in indoor environments[A], IEEE 2002 28th Annual Conference of the Industrial Electronics Society, Volume 4,5-8 Nov.2002:2587-2592.
[15]P. Beeson, N.K. Jong, B. Kuipers, Towards autonomous topological place detection using the extended voronoi graph[A], in:Proc. of the IEEE Int. Conf. on Robotics & Automation, 2005:1877-1983.
[16]A. Tapus, Topological SLAM:simultaneous localization and mapping with fingerprints of places [D], Swiss Federal Institute of Technology, Lausanne,2005.
[17]Byung Jae Park; Se-Jin Lee, Jong-Hwan Lim, Dong-Woo Cho, Angular Uncertainty Reduction of Sonar Range Data for a Grid-based Map Building[A],2006 International Joint Conference on SICE-ICASE, Oct.2006:2010-2014.
[18]Leeke, S., Maharatna, L., A low-power geometric mapping co-processor for high-speed graphics application[A],2006 IEEE International Symposium on Circuits and Systems, 21-24 May,2006:328-331.
[19]吴培良,孔令富,赵逢达,一种服务机器人家庭全息地图构建方法研究[J],计算机应用研究,2010(3)：982-986
[20]蔡自兴,邹小兵,移动机器人环境认知理论与技术的研究[J],机器人,2004(1)：87-92.
[21]Sebastian T, Wolfram B, Dieter F. A real-time algorithm for mobile robot mapping with application to multi-robot and 3D mapping [A], the IEEE int. conf. on robotics and automation.San Francisco,2000:1543-1551.
[22]Andrew J D, Nobuyuki K.3D simultaneous localization and map building using active vision for a robot moving on undulating terrain[A]. the IEEE computer society conference on computer vision and pattern recognition. Hawaii,2001:2811-2819.
[23]Se S, Lowe D, Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks [J]. Robotics research,2002,21(8):735-738.
[24]S. Thrun, Learning metric-topological maps for indoor mobile robot navigation[J], Artificial Intelligence,1998,99(1):21-71.
[25]N. Tomatis, I. Nourbakhsh and R. Siegwart, Hybrid Simultaneous Localization and Map Building:A Natural Integration of Topological and Metric[J], Robotics and Autonomous Systems,2003,44(7):3-14.
[26]A. Martinelli, A. Tapus, K.O. Arras and R. Siegwart, Multi-resolution SLAM for Real World Navigation[A], In Proceedings of the 11th International Symposium of Robotics Research, Siena, Italy,2003:449-456.
[27]王珂,王伟,庄严,孙传昱,基于几何—拓扑广域三维地图和全向视觉的移动机器人自定位,自动化学报[J],2008,34(11)：1369-1378.
[28]Sangwoo Jo, Qonita M. Shahab, Yong-Moo Kwon, Sang Chul Ahn, Indoor Modeling for Interactive Robot Service[A],2006 International Joint Conference on SICE-ICASE, Bexco, Busan, Korea,2006:3531-3536.
[29]梁志伟,马旭东,戴先中,房芳,基于分布式感知的移动机器人同时定位与地图创建[J],机器人,2009,31(1)：33-39.
[30]B. Peter, et al.,3D Modeling of Indoor Environments by a Mobile Robot with a Laser Scanner and Panoramic Camera [A], IEEE International Conference on Intelligent Robots and Systems (IROS 2004),2004, Vol.4:3430-3435.
[31]C. Galindo, A. Saffiotti, S. Coradeschi, P. Buschka, J.A. Fernandez-Madrigal and J. Gonz adlez, Multi-Hierarchical Semantic Maps for Mobile Robotics[A], Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), Edmonton, CA,2005:3492-3497.
[32]B. Kuipers, The Spatial Semantic Hierarchy[J], Artificial Intelligence,2000,119(5): 191-233.
[33]S. Vasudevan, S. Gachter, V. Nguyen, R. Siegwart, Cognitive maps for mobile robots_and object based approach[J], Robotics and Autonomous Systems,2007,55(3):359-371.
[34]G. Kruijff, H. Zender, P. Jensfelt, H. Christensen, Situated dialogue and spatial organization:What, where... and why?[J], Advanced Robotic System,2007,4 (2): 125-138.
[35]Arleo A, Gerstner W. Spatial cognition and neuro mimetic navigation:a model of hippocampal place cell activity [J]. Biological Cybernetics.2000,83 (3):287-299.
[361 E.C. Tolman, Cognitive maps in rats and men[J]. Psychological Review,1948,55(3): 189-208.
[37]B. J. Kuipers, The cognitive map:Could it have been any other way?[R], Spatial Orientation:Theory, Research, and Application. New York:Plenum Press,1983:345-359.
[38]W.K. Yeap and M.E. Jefferies, On early cognitive mapping[J], Spatial Cognition and Computation 2001,2(2):85-116.
[39]C. Stachniss,o. Marti nez-Mozos, A. Rottmann, and W. Burgard, Semantic Labeling of Places[A], In Proc. of the Int. Symposium of Robotics Research (ISRR).San Francisco, CA, USA,2005:89-95.
[40]A. Torralba, K. P. Murphy, W. T. Freeman and M. A. Rubin, Contextbased vision system for place and object recognition[A], Proceedings of the IEEE International Conference on Computer Vision, vol.1, Nice, France:191-203.
[41]Mallot H A. Spatial cognition, behavioral competences, neural mechanisms, and evolutionary scaling[J]. Kognition swissenschaft,1999,8(1):40-48.
[42]O. Mozos, P. Jensfelt, H. Zender, M. Kruijff, W. Burgard, From labels to semantics:An integrated system for conceptual spatial representations of indoor environments for mobile robots[R], in:ICRA Workshop:Semantic Information in Robotics,2007:395-403.
[43]H. Zender, P. Jensfelt, O. Martinez-Mozos, G.-J. Kruijff, W. Burgard, An integrated robotic system for spatial understanding and situated interaction in indoor environments[A],2007 AAAI,2007:1584-1589.
[44]P. Beeson, M. MacMahon, J. Modayil, A. Murarka, B. Kuipers, B. Stankiewicz, Integrating Multiple Representatios of Spatial Knowledge for Mapping, Navigation, and Communication[A], Interaction Challenges for Intelligent Assistants, AAAI Spring Symposium Series,2007:513-520.
[45]H. Zender, Learning spatial organization through situated dialogue[D], Saarland University, Dept. of Computational Linguistics, Saarbrcken, Germany,2006.
[46]C. Galindo, A. Saffiotti, S. Coradeschi, P. Buschka, J. Fernandez-Madrigal, J. Gonzalez, Multi-hierarchical semantic maps for mobile robotics[A], in:Proc of the IEEE/RSJ Int Conf on Intelligent Robots and Systems, IROS, Edmonton, CA,2005:3492-3497.
[47]D. Meger, P. Forssen, K. Lai, S. Helmer, S. McCann, T. Southey, M. Baumann, J. Little, D. Lowe, B. Dow, Curious george:An attentive semantic robot[R], in:IROS Workshop: From Sensors to Human Spatial Concepts,2007:390-404.
[48]A. Ranganathan, F. Dellaert, Semantic modeling of places using objects[A], in:Robotics: Science and Systems Conf.,2007:45-53.
[49]S. Vasudevan, S. Gachter, V.T. Nguyen, R. Siegwart, Cognitive maps for mobile robots— — an object based approach[J], Robotics and Autonomous Systems,2007,55 (5): 359-371.
[50]D. Anguelov, R. Biswas, D. Koller, B. Limketkai, S. Sanner, S. Thrun, Learning hierarchical object maps of non-stationary environments with mobile robots[A], in:Proc. of the Conf. on Uncertainty in Artificial Intelligence, UAI,2002:10-17.
[51]D. Anguelov, D. Koller, E. Parker, S. Thrun, Detecting and modeling doors with mobile robots[A], in:Proc. of the IEEE Int. Conf. on Robotics & Automation,2004:3777-3784.
[52]B. Limketkai, L. Liao, D. Fox, Relational object maps for mobile robots[A], in:Proc. of the Int. Conf. on Artificial Intelligence, IJCAI, Edinburgh, Scotland,2005:1471-1476.
[53]D.G. Lowe, Distinctive image features from scale-invariant keypoints[J], Computer Vision,2004,60(2):91-110.
[54]Shrihari Vasudevan, Viet Nguyen & Roland Siegwart, Towards a Cognitive Probabilistic Representation of Space for Mobile Robots[A],2006 IEEE International Conference on Information Acquisition August,2006, Weihai, Shandong, China:353-360.
[55]D. Anguelov, D. Koller, E. Parker, S. Thrun, Detecting and Modeling Doors with Mobile Robots[A], Proceedings of the International Conference on Robotics and Automation (ICRA),2004:636-642.
[56]D. Kortenkamp, L. D. Baker, and T. Weymouth, Using Gateways to Build a Route Map[A], Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems,1992:93-101.
[57]Claire D'Este, Claude Sammut, Learning and generalising semantic knowledge from object scenes[J], Robotics and Autonomous Systems,2008 (56:891-990.
[58]吴皓,田国会,黄彬,未知环境探测的多机器人协作策略研究[J],山东大学学报,2008.38(4)：27-31
[59]张洪峰,王硕,谭民,王龙,基于动态分区方法的多机器人协作地图构建[J],机器人,2003,25(2)：156-162.
[60]Chan Sze Kong. New Ai Peng, Ioannis Rekleitis, Distributed Coverage with Multi-Robot System[A], Proceedings of the 2006 IEEE International Conference on Robotics and Automation Orlando, Florida:2423-2430.
[61]Yamauchi B, Frontier-based exploration using multiple robots[A], Second International Conference on autonomous Agents,1998:47-53.
[62]Burgard W, Moors M, Fox D, et al, Collaborative multi robot exploration[A]. IEEE Int Conf on Robotics and Automation ICRA, San Francisco IEEE,2000:476-481.
[63]Ioannis R, Vicent L S, Ai PN, et al. Limited communication,multi-robot team based coverage[C]. Proceedings of the IEEE Intl. Conference on Robotics and Automation,2004: 3462-3467.
[64]Oriolo G, Ulivi G. Real-time map building and navigation for autonomous robots in unknown environments[J]. IEEE Trans Systems, Man, and Cybernetics,1998,28(3): 316-332.
[65]R.Grabowski,L.Navarro-Serment,C.Paredis,P.Khosla,Heterogeneous teams of modular robots for mapping and exploration[J], Autonomous Robots,2000,8(3):293-308.
[66]Zlot R, Stentz A, Dias M B, et al. Multi robot exploration controlled by a market economy[A]. Proc of the IEEE Int Conf on Robotics and Automation(ICRA)[C]. Washington IEEE Press,2002:3016-3023.
[67]高云园,韦巍,基于免疫机理的多机器人未知环境完全探测研究[J],模式识别与人工智能,2007.4：191-196.
[68]HowardA, MataricM.J. and SukhatmeG.S., Localization for Mobil Robot Teams Using Maximum Likelihood Estimation[C], International Conference on Intelligent Robot and Systems (IROS02), Volume:3,30 and Sept.2002:2849-2854.
[69]HowardA, Maja J, Sukhatme G. Localization for mobile robot teams:a distributed MLE approach[J]. Experimental Robotics Ⅷ, Bruno Siciliano and Paulo Dario(eds) Springer-Verlag,2003:146-155.
[70]S.I.Roumeliotis and G.A.Bekey,2002, Distributed Multi robot Localization[J], IEEE Transaction on Robotics and Automation,2002,18(5):533-540.
[71]Roumeliotis S, Bekey G. Collective localization:a distributed kalman filter approach [A]. Proceedings of the IEEE International Conference on Robotics and Automation [C], San Francisco, USA,2000,3:2958-2965.
[72]Andrew Howard, Multi-Robot Mapping using Manifold Representations[A], proceedings of the 2004 IEEE International Conference on Robot and Automation New Orleans, April,2004:49-57.
[73]Fox D, Burgard W, Kruppa Hetal. A probabilistic approach to collaborative multi -robot localization[J]. Autonomous Robots on Heterogeneous Multi-Robot Systems,2000,8(3): 325-344.
[74]王玲,邵金鑫,万建伟,刘云辉,多机器人定位中基于熵的分布式观测量选择方法[J],电子学报,2007.]：333-336.
[75]刘俊承,原魁,邹伟,朱海兵,基于特征粒子的Monte Carlo自定位方法,机器人,2006.1：30-35.
[76]Mourikis, A. I.; Roumeliotis, S. I.; Burdick, J. W.; SC-KF Mobile Robot Localization:A Stochastic Cloning Kalman Filter for Processing Relative-State Measurements[J], IEEE Transactions on Robotics and Automation,2007,23(4):717-730.
[77]姜健,赵杰,李力坤,面向群智能机器人系统的声音协作定向[J],自动化学报,2007.4：385-391.
[78]Terence Chung Hsin Sit, ZhengLiu, Marcelo H. Ang Jr., Winston Khoon Guan Seah, Multi-robot mobility enhanced hop-count based localization in ad hoc networks[J], Robotics and Autonomous Systems,2007(55):244-252.
[79]Sun Peigang, Zhao Hai, Luo Dingding, Zhang Xiaodan, Yin Zhenyu,Research on RSSI-based location in smart space[J], The transaction of electronics,2007.7:1240-1244.
[80]FAN Le-hao, QIU Xiao-hui, TANG Yi-bin, Discussion on self-location algorithm for wireless sensor networks[J], Telecommunications for Electric Power System,2007 (10): 19-23.
[81]Robert B. Johnston, Alvin Khin Choy Yap, Electronic Data Interchange using Two Dimensional Bar Code [D]. Department of Business Systems, Monash University, Australia,1998:235-242.
[82]中国标准出版社.条码国家标准汇编[M].北京：中国标准出版社,2004.
[83]G. Grisetti. C. Stachniss, and W. Burgard. Improved techniques for grid mapping with Rao-Blackwellized particle filters [J]. IEEE Trans. Robot.,2007,23(1):34-46.
[84]C. Stachniss, W. Burgard. Mapping and exploration with mobile robots using coverage maps[A]. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'2003),2003:467-472.
[85]Van Zwynsvoorde D, Simeon T. Alami R, Incremental topological modeling using local voronoi-like graphs [A], Proceeding of the 2000 IEEE/RSJ International Conference on Intelligent Robot s and Systems,2000:897-902.
|86] P. Beeson, N.K. Jong, B. Kuipers, Towards autonomous topological place detection using the extended voronoi graph [A]. Proceedings of the IEEE International Conference on Robotics and Automation,2005:4373-4379.
[87]J.L.Blanco, J.Gonz'alez, J.A.Fern'and ez-Madrigal, Subjective local maps for hybrid metric-topological SLAM[J], Robotics and Autonomous Systems,2009,57(3):64-74.
[88]Jose-Luis Blanco, Juan-Antonio Fernandez-Madrigal, and Javier Gonzalez, Toward a Unified Bayesian Approach to Hybrid Metric-Topological SLAM[J], IEEE Transaction on Robotics,2008,24(2):1824-1831.
[89]B. Krose, O. Vlassis, R. Bunschoten, Y. Motomura, A probabilistic model for appearance-based robot localization[J], Image and Vision Computing,2001,19(6): 381-391.
[901王珂,王伟,庄严,孙传昱,基于几何—拓扑广域三维地图和全向视觉的移动机器人自定位[J],自动化学报,2008,34(11)：1369-1378.
[91]Sangwoo Jo, Qonita M. Shahab, Yong-Moo Kwon, Sang Chul Ahn, Indoor Modeling for Interactive Robot Service[A],2006 International Joint Conference on SICE-ICASE, Bexco, Busan, Korea,2006,10:3531-3536.
[92]梁志伟,马旭东,戴先中,房芳,基于分布式感知的移动机器人同时定位与地图创建[J],机器人,2009,31(1)：33-39.
[93]B. Peter, et al.,3D Modeling of Indoor Environments by a Mobile Robot with a Laser Scanner and Panoramic Camera [A], IEEE International Conference on Intelligent Robots and Systems (IROS 2004),2004, Vol.4:3430-3435.
[94]Andreas Nuchter, Joachim Hertzberg, Towards semantic maps for mobile robots[J], Robotics and Autonomous Systems 2008,56(8):915_926.
[95]Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, et al, Towards 3D Point cloud based object maps for household environments[J], Robotics and Autonomous Systems 2008,56(8):927-941.
[96]D. Hanel, et al., Learning Compact 3D Models of Indoor and Outdoor Environments with a Mobile Robot [J], Robotics and Autonomous Systems,2003,44(1):15-27.
[97]Juan Mabuel Saez, Francisco Escolano, A Global 3D Map-Building Approach Using Stereo Vision[A]. Proceedings of the 2004 IEEE international Conference on Robotics &Automation New Orleans,2004, Vol.2:1197-1202.
[98]孙珺,基于地面移动机器人感知信息的三维地图构建[D],南京理工大学,2005.
[99]D. Scharstein, A.J. Briggs. Real-time recognition of self-similar landmarks[J]. Image and Vision Computing,2001,19(11):763-772.
[100]Craig Becker, Joaquin Salas, Kentaro Tokusei, Reliable Navigation Using Landmarks[A]. IEEE International Conference on Robotics and Automation, Montreal,1995:401-406.
[101]Yue Liu Mingjun Liu, Automatic Recognition Algorithm of Quick Response Code Based on Embedded System[A], Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06),2006:201-207.
[102]赵守鹏,基于服务机器人系统的智能空间关键技术研究与设计[D],山东大学,2007.
[103]周祥,孔晓东,曾贵华,一种新的基于Hough变换的椭圆轮廓检测方法[J],计算机工程,2007,8：166-168.
[104]David G. L Distinctive image features from scale-invariant key points[J], Computer Vision,2004,60(2):91-110.
[105]姜海涛,田国会,薛英花,基于新型人工地标的机器人主动控制[J],山东大学学报(工学版),2011,41(2)：236-240.
[106]Beis J S. Lowe D G, Shape indexing using approximate nearest-neighbor search in high-dimensional spaces [C], Proceedings of the IEEE 1997 Computer Society Conference on Computer Vision and Pattern Recognition,1997:1000-1006.
[107]Scharstein D, Szeliski R. y, A Taxonomy and Evaluation of Dense Two-frame Stereo Correspondence Algorithms[J], Computer Vision,2002,47(1):7-42.
[108]Kang-Hyun Kim, Heesung Jun. Realistic 3D Reconstruction From An Image Sequence[A]. Proceedings of the 7th Koren-Russio International Symposium, KORUS 2003:563-570.
[109]P.H.S. Torr, A.W. Fitzgibbon. Invariant Fitting of Two View Geometry[J]. IEEE Transactions On Pattern Analysis And Machine Intelligence,2004,26(5):648-650.
[110]卜范骞,基于智能空间的服务机器人定位与构图技术研究[D],山东大学,2008.
[111]Rui Rocha, Jorge Dias. Adriano Carvalho, Cooperative multi-robot systems:A Study of Vision-Based 3-D Mapping Using Information Theory[J]. Robotics and Autonomous System,2005(53):282-311.
[112]Mikolajczyk K. and Schmid C, An affine invariant interest point detector[A], In European Conference on Computer Vision (ECCV), Copenhagen, Denmark,2002:128-142.
[113]吴福朝.计算机视觉中的数学方法[M]，北京：科学出版社,2008.
[114]谷永发,三维场景重建的理论研究和工程实践[D],西安电子科技大学,2009.
[115]K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors[A]. In International Conference on Computer Vision & Pattern Recognition.2003; volume 2: 257-263.
[116]G. Shafer, A mathematical theory of evidence. Princeton[D], Princeton University,1974.
[117]J. Dezert, F. Smarandache. On the generation of hyper-power sets for the DSmT[A], Proceedings of The 6th Int. Conf. Information Fusion. Australia, Cairns, Queensland.8-11, 2003:1118-1125.
[1181李新德,多源不完善信息融合方法及其应用研究[D].华中科技大学,2007
[119]J. Pearl. Reasoning with belief functions:an analysis of compatibility[J], Approximate Reasoning,1990,4:363-389.
[120]C. K. Murphy. Combining belief functions when evidence conflicts[J]. Decision Support Systems,2000,29:1-9.
[121]杨锦园,黄心汉,李鹏,基于DSmT的移动机器人地图构建及传感器管理[J],计算机科学,2010,27(4)：227-233.
[122]李新德,黄心汉,王敏,基于经典DSmT的Sonar栅格地图创建[J],计算机应用研究,2007,24(3)：209-213.
[123]F.Smarandache, J.Dezert. Advances and Applications of DSmT for information Fusion[F]. Vol.2. Rehoboth, USA:American Research Press,2006:450-469.
[124]Andreas Nuchter, Joachim Hertzberg, Towards semantic maps for mobile robots[J], Robotics and Autonomous System,2008,56(11):915-926.
[125]Wolf, D.F.; Sukhatme, G.S., Semantic Mapping Using Mobile Robots[J], IEEE Transactions on robotics,2008,24(2):245-258.
[126]Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, Mihai Dolha, Michael Beetz, Towards 3D Point cloud based object maps for household environments[J], Robotics and Autonomous Systems,2008,56(11):927-941.
[127]赵守鹏,田国会,李晓磊,基于单个人工地标的机器人自主定位[J],山东大学学报(工 学版),2007,37(4)：39-45.
[128]穴洪涛,田国会,李晓磊,路飞,QR Code在多种类物品识别与操作中的应用[J],山东大学学报(工学版),2007,37(6)：25-31.
[129]A. Ng, M. Jordan, Y. Weiss, on spectral clustering:Analysis and an algorithm[A], in:Proc. Advances in Neural Information Proceeding Systems,2001:345-352.
[130]Ogawa, Y., Shimada, N., Shirai, Y., Environmental mapping for mobile robot by tracking SIFT feature Points using trinocular vision[A], Annual Conference on SICE, Kagawa University, Japan,2007:1996-2001.
[131]D. Scharstein, A.J. Briggs. Real-time recognition of self-similar landmarks[J]. Image and Vision Computing,2001(11):763-772.
[132]SI Bing-yu, Research on the problem concerning visual artificial landmark based navigation of autonomous mobile robot[D], ShenYang:Dongbei University,2003.
[133]Oscar Martinez Mozos, Rudolph Triebel, Patric Jensfelt, Axel Rottmann, Wolfram Burgard, Supervised semantic labeling of places using information extracted from sensor data[J], Robotics and Autonomous Systems,2007(5):391-402.
[134]DissanayakeM W M G, Newman P'Clark S, et al. A solution to the simultaneous localization and map building(SLAM)problem[J]. IEEE Transactions on Robotics and Automation,2001,17(3):229-241.
[135]郭剑辉,赵春霞,石杏喜,一种改进的联合相容SLAM数据关联方法[J],仪器仪表学报,2008,29(11)：2260-2265.
[136]Davey S J. Simultaneous localization and map building using the probabilistic multi— hypothesis tracker[J]. IEEE Transactions on Robotics,2007,23(2):271-280.
[137]周武,赵春霞,SLAM问题的一种优化数据关联算法[J],机器人,2009(5)：216-223.
[138]郑宏,王景川,陈卫东,基于地图的移动机器人自定位与导航系统[J],机器人,2009,29(4)：397-402.
[139]韩金华,王立权,韩宗真,孟庆鑫,裘运根,护士助手机器人在结构化环境中的导航系统研究[J],哈尔滨工业大学学报,2007,39(7)：1166-1171.
[140]王珂,王伟,庄严,孙传昱,基于几何-拓扑广域三维地图和全向视觉的移动机器人自定位[J],自动化学报,2008,34(]1)：1369-1378.
[141]石朝侠,洪炳镕,周彤,王燕清,大规模环境下的拓扑地图创建与导航,机器人,2007,29(5)：433-439.
[142]LiuJuan, CaiZixing, TuChunming. A Connectionist Approach for Cognitive Map Learning and Navigation Based on Spatio-Temporal Experiences[J], Control Theory and Applications,2003,20(2):161-167.
[143]S. Vasudevan and R. Siegwart, A Bayesian approach to Conceptualization and Place Classification:Incorporating Spatial Relationships (distances) to Infer[R], Proceedings of the IEEE IROS, FS2HSC.2007:55-68.
[144]S. Vasudevan. V. Nguyen, and R. Siegwart, Cognitive Maps for Mobile Robots-An Object based Approach[A], Proceedings of the IROS.2006:7-12.
[145]Chuho Yi.ⅡHong Suh, Gi Hyun Lim, and Byung-Uk Choi, Bayesian Robot Localization using Spatial Object Contexts[A], The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems October 11-15,2009 St. Louis. USA:3467-3474.
1146]司秉玉.基于可视人工路标的自主机器人导航若干问题研究[D]，东北大学，2003.
[147]张明状,庄严,王伟,顾明伟.移动机器人及时环境探索与地图库构建[J],东南大学学报(自然科学版),2009(39)：111-115.
[148]Varela F J.&Coutinho A.& Dupire E.&Vaz, Cognitive Networks:Immune, Neural and Otherwise[J]. Theoretical Immunology,1988:359-375.
[149]Varela F J.&Coutinho A.Second generation immune networks[J], Imm. Today, 1991,12(5):159-166.
[150]吕岗,陈小平,谭得健.免疫算法抗体浓度调节定义的改进[J].数据采集与处理,2003,18(1)：44-48.
[151]Burgard W, Moors M, Fox D, et al, Collaborative multi robot exploration[A]. IEEE Int Conf on Robotics and Automation ICRA, San Francisco IEEE,2000:476-481.
[152]Ioannis R, Vicent L S, Ai PN, et al. Limited communication,multi-robot team based coverage[C]. Proceedings of the IEEE Intl. Conference on Robotics and Automation,2004: 3462-3467.
[153]Zlot R, Stentz A, Dias M B, et al. Multi robot exploration controlled by a market economy[A]. Proc of the IEEE Int Conf on Robotics and Automation(ICRA)[C]. Washington IEEE Press,2002:3016-3023.
[154]Alejandro R.Mosteo, Luis Montano, Comparative experiments on optimization criteria and algorithms for auction based multi-robot task allocation[A],2007 IEEE International Conference on Robotics and Automation, Roma,Italy,2007:3345-3350.
[155]张飞,陈卫东,席裕庚,多机器人协作探索的改进市场法[J],控制与决策,2005,20(5)：516-524.
[156]高云园,韦巍,基于免疫机理的多机器人未知环境完全探测研究[J],模式识别与人工智能,2007.4：191-196.
[157]WeihuaSheng, QingyanYang, JindongTan, NingXi, Distributed multirobot coordination in area exploration [J], Robotics and Autonomous Systems 54(2006):945-955.
[158]Hyun-Deok Kang,Kang-Hyun Jo. Self-localization of mobile robot using Omni-direcional vision[C]. Proceedings of the 7th Korea-Russia International Symposium on Science and Technology, Korea, vol.2, No.28,2003, pp.86-92.
[159]S.Thrun, Y.Liu, D.Koller, A.Y.Ng, Z.Ghahramani, H.Durrant Whyte, Simultaneous localization and mapping with sparse extended information filters[J], Robotics Research, 2004(23):693-716.
[160]Elfes A, Occupancy grids:A stochastic spatial representation for active robot perception[A], In Proc.6th Conference on Uncertainty in AI.1990:60-70.
[161]Yamauchi B, Frontier-based exploration using multiple robots[C], Second International Conference on autonomous Agents,1998:47-53.
[162]N.K. Jerne, Idiotopic Network and Other Preconceived Ideas[J], Immunological Rev., 1984,79:5-24.
[163]Jin-Hyung Jun; Dong-Wook Lee; Kwee-Bo Sim; Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system [A], IEEE SMC'99 Conference Proceedings,1999:614-619.
[164]周光明,贾梦雷,陈宗海,移动机器人未知环境自主探测的一种高效方法[J],上海交通大学学报,2005,39(6)：936-940.
[165]D.Fox, W.Burgard, H.Kruppa, S.Thrun,2000, A Probabilistic Approach to Collaborative Multi-Robot Localization [J], Autonomous Robots,2000,8:325-344.
[166]Juncheng Liu, Kui Yuan, Wei Zou, Qinmin Yang, Monte Carlo Multi-Robot Localization Based on Grid Cells and Characteristic Particles[C], Proceedings of the 2005IEEE/ASME International Conference on Advanced Intelligent Mechatronics, California, USA,24-28 July,2005:510-515.
[167]HowardA, MataricM.J. and SukhatmeG.S., Localization for Mobil Robot Teams Using Maximum Likelihood Estimation[C], International Conference on Intelligent Robot and Systems (IROS02), Volume:3,30 and Sept.-5Oct.2002:2849-2854.
[168]H.Zhao and R.Shibasaki, A novel system for tracking pedestrians using multiple single-row laser-range scanners [J]. IEEE Trans. Syst., Man, Cybern.A,2005,35(2): 283-291.
[169]Terence Chung Hsin Sit, ZhengLiu, Marcelo H. Ang Jr., Winston Khoon Guan Seah, Multi-robot mobility enhanced hop-count based localization in ad hoc networks[J], Robotics and Autonomous Systems,2007,55:244-252.
[170]Sun Peigang, Zhao Hai, Luo Dingding, Zhang Xiaodan, Yin Zhenyu, Research on RSSI-based location in smart space[J], The transaction of electronics,2007.7:1240-1244.
[171]FAN Le-hao, QIU Xiao-hui, TANG Yi-bin, Discussion on self-location algorithm for wireless sensor networks[J], Telecommunications for Electric Power System,2007(10): 19-23.
[172]Erin-Ee-Lin Lau, Wan-Young Chung,Enhanced RSSI-based Real-time User Location Tracking System for Indoor and Outdoor Environments[C],2007 International Conference on Convergence Information Technology,2007:1213-1219.
[173]Ioannis M. Rekleitis, Gregory Dudek, Evangelos E.Milios, Multi-robot cooperative localization:a study of trade-offs between efficiency and accuracy[C], Proceedings of the 2002 IEEE Conference on Intelligent Robots and Systems, Switzerland,2002:2690-2695.