We review the use of factor graphs for the modeling and solving of large-scale inference problems in robotics. Factor graphs are a family of probabilistic graphical models, other examples of which are Bayesian networks and Markov random fields, well known from the statistical modeling and machine learning literature. They provide a powerful abstraction that gives insight into particular inference problems, making it easier to think about and design solutions, and write modular software to perform the actual inference. We illustrate their use in the simultaneous localization and mapping problem and other important problems associated with deploying robots in the real world. We introduce factor graphs as an economical representation within which to formulate the different inference problems, setting the stage for the subsequent sections on practical methods to solve them.We explain the nonlinear optimization techniques for solving arbitrary nonlinear factor graphs, which requires repeatedly solving large sparse linear systems. The sparse structure of the factor graph is the key to understanding this more general algorithm, and hence also understanding (and improving) sparse factorization methods. We provide insight into the graphs underlying robotics inference, and how their sparsity is affected by the implementation choices we make, crucial for achieving highly performant algorithms. As many inference problems in robotics are incremental, we also discuss the iSAM class of algorithms that can reuse previous computations, re-interpreting incremental matrix factorization methods as operations on graphical models, introducing the Bayes tree in the process. Because in most practical situations we will have to deal with 3D rotations and other nonlinear manifolds, we also introduce the more sophisticated machinery to perform optimization on nonlinear manifolds. Finally, we provide an overview of applications of factor graphs for robot perception, showing the broad impact fa

Stanford_CS224W_Machine Learning with Graphs.zip

Origin Python示例 使用originpro Python软件包与Origin软件进行交互的代码示例。 这些示例可以与Origin中的内置Python解释器一起使用，也可以与外部Python解释器一起使用。 所有的例子将作为与嵌入式解释。 当将它们与外部解释器一起使用时，需要进行一些简单的修改，如 。 运行示例 运行这些示例的最简单方法是。 然后，只需解压缩文件并从Code Builder中的File菜单中打开所需的文件。 然后，按F5键运行示例。 请注意，某些示例要求安装其他Python软件包。 文献资料 对于嵌入式Python解释器，。 对于外部Python解释器，。 有关originpro软件包的文档，。

数据结构和算法专业化 我的Coursera数据结构和算法专业化作业解决方案

Focuses on classical problems in graph theory, including the 5-flow conjectures, the edge-3-colouring conjecture, the 3-flow conjecture and the cycle double cover conjecture. The text highlights the interrelationships between graph colouring, integer flow, cycle covers and graph minors. It also concentrates on graph theoretical methods and results.

分布式计算-PySpark 该存储库包含有关在Python中使用Spark进行分布式计算的微型项目。 文本分析：PySpark中的逐点相互信息 计算文本文件中出现的所有单词的一个或多个标记的PMI。 图/网络分析：PySpark中的个性化PageRank算法 实现PageRank算法的修改版本，其中参照给定的源节点执行排名。 修改有两个方面： 随机仅跳到源节点 由于节点悬空而造成的质量损失将完全转移到源节点，而不是在整个图形上重新分配 使用Spark数据帧和Spark SQL查询TPCH

力导向图的可视化 该项目实现了力导向图可视化算法。 执照 该项目是根据许可的，可以在找到其副本。

相对关系型数据库和NoSQL数据库，定义良好的图数据库在应对信息爆炸和社交网络数据上，具有一些独特的优势。本资源可以学习一些关键性的概念，以及实例参考。

This book is concerned with results in graph theory in which linear algebra and matrix theory play an important role. Although it is generally accepted that linear algebra can be an important component in the study of graphs, traditionally, graph theorists have remained by and large less than enthusiastic about using linear algebra. The results discussed here are usually treated under algebraic graph theory, as outlined in the classic books by Biggs [20] and by Godsil and Royle [39]. Our emphasis on matrix techniques is even greater than what is found in these and perhaps the subject matter discussed here might be termed linear algebraic graph theory to highlight this aspect.

图２．２　无线电波传播 在同一时刻，空间电磁波相位相同的点连成的面称为电磁波的等相面；而同一时刻振幅相 同的点连成的面称为等幅面。若等相面与等幅面重合，则称其为均匀电磁波，这时在等相面 上，电波具有相同的振幅。 按等相面的形状，电磁波可分为平面波、球面波和柱面波等。由点辐射源产生的电磁波即 为球面波，而到了无穷远处，由于球面波的曲率很小，可将其近似为平面波。图２．２即为平面 波的例子。 无线电波的工作频率可以从几 Ｈｚ到３０００ＧＨｚ，对应的波长从几万ｋｍ到０１ｍｍ。按 ·０２· 无线电导航原理与系统