利用网络表征学习辨识复杂网络节点影响力

发现复杂网络中最具影响力的节点,有助于分析和控制网络中的信息传播,具有重要的理论意义和实用价值.传统的确定节点影响力的方法大多基于网络的邻接矩阵、拓扑结构等,普遍存在数据维度高和数据稀疏的问题,基于网络表征学习,本文提出了一种局部中心性指标来辨识网络中高影响节点(NLC),首先采用DeepWalk算法,把高维网络中的节点映射为一个低维空间的向量表示,并计算局部节点对之间的欧氏距离;接着根据网络的拓扑结构,计算每个节点在信息的传播过程中,对所在局部的影响力大小,用以识别高影响力节点.在八个真实网络中,以SIR和SI传播模型作为评价手段,将NLC算法和度中心性、接近中心性、介数中心性、邻居核中心性、半局部中心性做了对比,结果表明NLC算法具有良好的识别高影响力传播节点的性能.     

基于Voronoi地图表示方法的同步定位与地图创建

针对基于混合米制地图机器人同步定位与地图创建 (Simultaneous localization and mapping, SLAM)中地图划分方法不完善的问题, 提出了基于Voronoi地图表示方法的同步定位与地图创建算法VorSLAM. 该算法在全局坐标系下创建特征地图, 并根据此特征地图使用Voronoi图唯一地划分地图空间, 在每一个划分内部创建一个相对于特征的局部稠密地图. 特征地图与各个局部地图最终一起连续稠密地描述了环境. Voronoi地图表示方法解决了地图划分的唯一性问题, 理论证明局部地图可以完整描述该划分所对应的环境轮廓. 该地图表示方法一个基本特点是特征与局部地图一一对应, 每个特征都关联一个定义在该特征上的局部地图. 基于该特点, 提出了一个基于形状匹配的数据关联算法, 用以解决传统数据关联算法出现的多重关联问题. 一个公寓弧形走廊的实验验证了VorSLAM算法和基于形状匹配的数据关联方法的有效性.     

Iterated D-SLAM map joining: evaluating its performance in terms of consistency,accuracy and efficiency

This paper presents a new map joining algorithm and a set of metrics for evaluating the performance of mapping techniques. The input to the new map joining algorithm is a sequence of local maps containing the feature positions and the final robot pose in a local frame of reference. The output is a global map containing the global positions of all the features but without any robot poses. The algorithm is built on the D-SLAM mapping algorithm (Wang et al. in Int. J. Robot. Res. 26(2):187–204, 2007) and uses iterations to improve the estimates in the map joining step. So it is called Iterated D-SLAM Map Joining (I-DMJ). When joining maps I-DMJ ignores the odometry information connecting successive maps. This is the key to I-DMJ efficiency, because it makes both the information matrix exactly sparse and the size of the state vector bounded by the number of features.     

An Iterative Pose Estimation Algorithm Based on Epipolar Geometry With Application to Multi-Target Tracking

This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit (IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking (MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/HhK-p2hXNnU.       

Attributed concept maps: fuzzy integration and fuzzy matching

A concept map, typically depicted as a connected graph, is composed of a collection of propositions. Each proposition forming a semantic unit consists of a small set of concept nodes interconnected to one another with relation links. Concept maps possess a number of appealing features which make them a promising tool for teaching, learning, evaluation, and curriculum planning. We extend concept maps by associating their concept nodes and relation links with attribute values which indicate the relative significance of concepts and relationships in knowledge representation. The resulting maps are called attributed concept maps (ACM). Assessing students will be conducted by matching their ACMs with those prebuilt by experts. The associated techniques are referred to as map matching techniques. The building of an expert ACM has in the past been done by only one specialist. We integrate a number of maps developed by separate experts into a single map, called the master map (MM), which will serve as a prototypical map in map matching. Both map integration and map matching are conceptualized in terms of fuzzy set discipline. Experimental results have shown that the proposed ideas of ACM, MM, fuzzy map integration, and fuzzy map matching are well suited for students with high performances and difficult subject materials.     

基于混合位姿估计模型的移动机器人三维地图创建方法

针对室内复杂环境下的稠密三维建模问题, 提出一种基于RGB-D 相机的移动机器人同时定位与三维地图创建方法. 该方法利用架设在移动机器人上的RGB-D 相机获取环境信息, 根据点云和纹理加权模型建立结合局部纹理约束的混合位姿估计方法, 确保定位精度的同时减小失败率. 在关键帧选取机制下, 结合视觉闭环检测方法, 运用树结构网络优化(TORO) 算法最小化闭环误差, 实现三维地图的全局一致性优化. 在室内环境下的实验结果验证了所提出算法的有效性和可行性.

     

Knowledge maps for e-learning

Maps such as concept maps and knowledge maps are often used as learning materials. These maps have nodes and links, nodes as key concepts and links as relationships between key concepts. From a map, the user can recognize the important concepts and the relationships between them. To build concept or knowledge maps, domain experts are needed. Therefore, since these experts are hard to obtain, the cost of map creation is high. In this study, an attempt was made to automatically build a domain knowledge map for e-learning using text mining techniques. From a set of documents about a specific topic, keywords are extracted using the TF/IDF algorithm. A domain knowledge map (K-map) is based on ranking pairs of keywords according to the number of appearances in a sentence and the number of words in a sentence. The experiments analyzed the number of relations required to identify the important ideas in the text. In addition, the experiments compared K-map learning to document learning and found that K-map identifies the more important ideas.     

Topological Semantic Mapping and Localization in Urban Road Scenarios

Autonomous vehicle self-localization must be robust to environment changes, such as dynamic objects, variable illumination, and atmospheric conditions. Topological maps provide a concise representation of the world by only keeping information about relevant places, being robust to environment changes. On the other hand, semantic maps correspond to a high level representation of the environment that includes labels associated with relevant objects and places. Hence, the use of a topological map based on semantic information represents a robust and efficient solution for large-scale outdoor scenes for autonomous vehicles and Advanced Driver Assistance Systems (ADAS). In this work, a novel topological semantic mapping and localization methodology for large-scale outdoor scenarios for autonomous driving and ADAS applications is presented. The methodology uses: (i) a deep neural network for obtaining semantic observations of the environment, (ii) a Topological Semantic Map (TSM) for storing selected semantic observations, and (iii) a topological localization algorithm which uses a Particle Filter for obtaining the vehicle’s pose in the TSM. The proposed methodology was tested on a real driving scenario, where a True Estimate Rate of the vehicle’s pose of 96.9% and a Mean Position Accuracy of 7.7[m] were obtained. These results are much better than the ones obtained by other two methods used for comparative purposes. Experiments also show that the method is able to obtain the pose of the vehicle when its initial pose is unknown.     

A Minimalistic Approach to Appearance-Based Visual SLAM

This paper presents a vision-based approach to simultaneous localization and mapping (SLAM) in indoor/outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omnidirectional vision sensor, a novel method is introduced based on the relative similarity of neighboring images. This new method does not require the determination of distances to image features using multiple-view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle different environments (without modification of the parameters), and it can cope with violations of the “flat floor assumption” to some degree and scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g., for solving the multirobot SLAM problem with unknown initial poses.      

Automatic metro map layout using multicriteria optimization

This paper describes an automatic mechanism for drawing metro maps. We apply multicriteria optimization to find effective placement of stations with a good line layout and to label the map unambiguously. A number of metrics are defined, which are used in a weighted sum to find a fitness value for a layout of the map. A hill climbing optimizer is used to reduce the fitness value, and find improved map layouts. To avoid local minima, we apply clustering techniques to the map-the hill climber moves both stations and clusters when finding improved layouts. We show the method applied to a number of metro maps, and describe an empirical study that provides some quantitative evidence that automatically-drawn metro maps can help users to find routes more efficiently than either published maps or undistorted maps. Moreover, we have found that, in these cases, study subjects indicate a preference for automatically-drawn maps over the alternatives.     

Node and edge selectivity estimation for range queries in spatial networks

Modern applications requiring spatial network processing pose several interesting query optimization challenges. Spatial networks are usually represented as graphs, and therefore, queries involving a spatial network can be executed by using the corresponding graph representation. This means that the cost for executing a query is determined by graph properties such as the graph order and size (i.e., number of nodes and edges) and other graph parameters. In this paper, we present novel methods to estimate the number of nodes and edges in regions of interest in spatial networks, towards predicting the space and time requirements for range queries. The methods are evaluated by using real-life and synthetic data sets. Experimental results show that the number of nodes and edges can be estimated efficiently and accurately, with relatively small space requirements, thus providing useful information to the query optimizer.     

Sparse Local Submap Joining Filter for Building Large-Scale Maps

This paper presents a novel local submap joining algorithm for building large-scale feature-based maps: sparse local submap joining filter (SLSJF). The input to the filter is a sequence of local submaps. Each local submap is represented in a coordinate frame defined by the robot pose at which the map is initiated. The local submap state vector consists of the positions of all the local features and the final robot pose within the submap. The output of the filter is a global map containing the global positions of all the features as well as all the robot start/end poses of the local submaps. Use of an extended information filter (EIF) for fusing submaps makes the information matrix associated with SLSJF exactly sparse. The sparse structure together with a novel state vector and covariance submatrix recovery technique makes the SLSJF computationally very efficient. The SLSJF is a canonical and efficient submap joining solution for large-scale simultaneous localization and mapping (SLAM) problems that makes use of consistent local submaps generated by any reliable SLAM algorithm. The effectiveness and efficiency of the new algorithm is verified through computer simulations and experiments.      

Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts

This paper presents a novel vision-based global localization that uses hybrid maps of objects and spatial layouts. We model indoor environments with a stereo camera using the following visual cues: local invariant features for object recognition and their 3D positions for object pose estimation. We also use the depth information at the horizontal centerline of image where the optical axis passes through, which is similar to the data from a 2D laser range finder. This allows us to build our topological node that is composed of a horizontal depth map and an object location map. The horizontal depth map describes the explicit spatial layout of each local space and provides metric information to compute the spatial relationships between adjacent spaces, while the object location map contains the pose information of objects found in each local space and the visual features for object recognition. Based on this map representation, we suggest a coarse-to-fine strategy for global localization. The coarse pose is estimated by means of object recognition and SVD-based point cloud fitting, and then is refined by stochastic scan matching. Experimental results show that our approaches can be used for an effective vision-based map representation as well as for global localization methods.     

分布式多维标度定位算法的研究

针对经典MDS-MAP定位算法在定位精度和算法复杂度方面的不足,提出一种分布式多维标度定位算法。改进后的算法加入了分簇的思想,将大规模网络分成多个具有簇首的局部网络。局部定位时,引入Hop-Euclidean算法,计算簇内节点间距离,再用局部网络融合算法将局部相对坐标图合并成全局相对坐标图。仿真分析表明,提出的算法在各向同性和各向异性网络中都有很好的定位精度,而且在定位精度提高的情况下可用于不规则网络,有利于网络的扩展,更适用于大规模密集型网络。     

改进平衡优化器算法的WSN覆盖优化

针对无线传感器网络在节点部署过程中存在节点覆盖空白及重叠覆盖的问题,提出一种改进平衡优化器算法（IEO）的网络覆盖优化。首先,利用环绕反向学习提高初始化种群质量,增强算法的优化能力;其次,引入动态正余弦因子进一步平衡全局搜索与局部开发能力,促使粒子种群对搜索空间中进行广泛搜索和深度挖掘;最后,通过在浓度更新阶段加入Circle混沌映射增加种群多样性,提高算法逃离局部最优的能力。实验结果表明,将IEO算法应用于WSN的覆盖优化实验中,与标准平衡优化器算法及其他改进算法相比,有效降低部署成本,表现出更高的网络覆盖率,改善网络的监测质量。     

The Effects of Stitching Orders in Patch-and-Stitch WSN Localization Algorithms

A "patch-and-stitch” localization algorithm divides the network into small overlapping subregions. Typically, each subregion consists of a node and all or some of its neighbors. For each subregion, the algorithm builds a local map, called a patch, which is actually an embedding of the nodes it spans in a relative coordinate system. Finally, the algorithm stitches those patches to form a single global map. In a patch-and-stitch algorithm, the stitching order makes an influence on both the performance and the complexity of the algorithm. In this paper, we present a formal framework to deal with stitching orders in patch-and-stitch localization algorithms. In our framework, the stitching order is determined by a stitching scheme and the stitching scheme consists of a stitching policy and a potential function. The potential function is to predict how well a patch will be stitched if patches are stitched according to a given partial order. The stitching policy is a mechanism that determines the stitching order based on the predictions by the potential function. We present various stitching schemes and evaluate them through simulations. In addition, we apply the patch-and-stitch strategy into the anchor-based localization and propose a clustering-based localization algorithm. A potential function is used to partition the network into clusters each of which is centered at an anchor node. For each cluster, a cluster map is constructed via the anchor-free localization algorithm. Then, those cluster maps are combined to form a single global map. We propose a stitching technique for combining those cluster maps and analyze the performance of the algorithm by simulations.     

一种基于节点嵌入表示学习的社区搜索算法

针对已有社区搜索算法采用高维稀疏向量表示节点时间复杂度高的问题,提出一种基于节点嵌入表示学习的社区搜索算法CSNERL.节点嵌入技术能够直接从网络结构中学习节点的低维实值向量表示,为社区搜索提供了新思路.首先,针对已有节点嵌入算法存在较高概率在最亲近邻居间来回游走的问题,提出基于最亲近邻居但不立即回访随机游走的节点嵌入模型NECRWNR,采用NECRWNR模型学习节点的特征向量表示;然后,采用社区内所有节点的向量均值作为社区的向量表示,通过选择与当前社区距离最近的节点加入社区的方法实现一种新的社区搜索算法.在真实网络和模拟网络数据集上分别与相关的社区搜索算法进行实验对比,结果表明所提出社区搜索算法CSNERL具有更高的准确性.     

Real-time building of a thinning-based topological map

An accurate and compact map is essential to an autonomous mobile robot system. A topological map, one of the most popular map types, can be used to represent the environment in terms of discrete nodes with edges connecting them. It is usually constructed by Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map by using a thinning algorithm. This algorithm, when combined with the application of the C-obstacle, can easily extract only the meaningful topological information in real-time and is robust to environment change, because this map is extracted from a local grid map generated based on the Bayesian update formula. In this paper, position probability is defined to evaluate the quantitative reliability of the end node extracted by the thinning process. Since the thinning process builds only local topological maps, a global topological map should be constructed by merging local topological maps according to nodes with high position probability. For real and complex environments, experiments showed that the proposed map building method based on the thinning process can accurately build a local topological map in real-time, with which an accurate global topological map can be incrementally constructed.     

Localization from connectivity in sensor networks

We propose an approach that uses connectivity information - who is within communications range of whom - to derive the locations of nodes in a network. The approach can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor nodes, if it is available. It is based on multidimensional scaling (MDS), an efficient data analysis technique that takes O(n/sup 3/) time for a network of n nodes. Unlike previous approaches, MDS takes full advantage of connectivity or distance information between nodes that have yet to be localized. Two methods are presented: a simple method that builds a global map using MDS and a more complicated one that builds small local maps and then patches them together to form a global map. Furthermore, least-squares optimization can be incorporated into the methods to further improve the solutions at the expense of additional computation. Through simulation studies on uniform as well as irregular networks, we show that the methods achieve more accurate solutions than previous methods, especially when there are few anchor nodes. They can even yield good relative maps when no anchor nodes are available.