RI: Inference in Large-Scale Graphical Models

RI：大规模图形模型中的推理

• 批准号: 0713162
• 负责人: Frank Dellaert
• 金额: $ 9.84万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713162&HistoricalAwards=false
• 关键词: RI; Inference; Large; Scale; Graphical

AbstractIn this project, we will develop novel methods to enable inference in large-scale graphical models, emphasizing the construction of models of unstructured environments from a vast number of sensor measurements. Creating models of the world from large amounts of noisy sensor data is an inference problem of vast proportions for which current methods do not scale up well. In keeping with the most recent literature, we model such inference problems using graphical models. However, in contrast to the literature we use factor graphs rather than belief nets, and show that there is a close and hithereto under-exploited connection between Factor Graphs and the sparse linear algebra literature. This connection enables cross- fertilization between inference in graphical models and sparse linear algebra. In particular, we will develop a novel graphical model paradigm, the BayesTree, inspired by the representations used in the so-called multifrontal factorization methods from sparse linear algebra.In terms of intellectual merits, these developments are novel and are expected to significantly advance the areas of large-scale mapping and 3D modeling in the fields of robotics and computer vision. However, we expect these new classes of algorithms to have broad impact beyond robotics in vision, in every fields where vast amounts of data needs to be processed and condensed in a parametric model. We expect the new graphical language we introduce to significantly improve understanding of exact inference in graphical models, as we feel this has been largely inaccessible but to advanced researchers in the field. By stressing the connections between the modest Gaussian elimination algorithm from linear algebra and more advanced inference methods such as the junction tree algorithm, we hope to enable a new generation of researchers that will truly understand these connections and hence be able to make revolutionary contributions in many fields.Progress reports will be regularly updated at http:// www.cc.gatech.edu/~dellaert/graphs/

AbstractIn这个项目中，我们将开发新的方法，使推理在大规模的图形模型，强调从大量的传感器测量的非结构化环境的模型的建设。从大量嘈杂的传感器数据中创建世界模型是一个大比例的推理问题，目前的方法不能很好地扩展。为了与最新的文献保持一致，我们使用图形模型来模拟这样的推理问题。然而，与文献相比，我们使用因子图而不是信念网，并表明因子图和稀疏线性代数文献之间存在密切的未充分利用的联系。这种联系使得图形模型和稀疏线性代数的推理之间能够相互促进.特别是，我们将开发一种新的图形模型范例，贝叶斯树，灵感来自于稀疏线性代数中所谓的多面分解方法中使用的表示。就智力价值而言，这些发展是新颖的，预计将显着推进机器人和计算机视觉领域的大规模映射和3D建模领域。 然而，我们预计这些新的算法类别将在视觉机器人之外产生广泛的影响，在每个需要在参数模型中处理和压缩大量数据的领域。我们希望我们引入的新图形语言能够显着提高对图形模型中精确推理的理解，因为我们认为这在很大程度上是无法访问的，但对于该领域的高级研究人员来说。通过强调线性代数中的适度高斯消去算法与更高级的推理方法（如连接树算法）之间的联系，我们希望能够使新一代的研究人员真正理解这些联系，从而能够在许多领域做出革命性的贡献。进度报告将定期更新www.cc.gatech.edu/~dellaert/graphs/