Factor Graphs: Exploiting Structure in Robotics
Factor Graphs: Exploiting Structure in Robotics
复制标题
因子图：利用机器人技术中的结构
DOI：
10.1146/annurev-control-061520-010504
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
F. Dellaert
中科院分区：
文献类型：
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作者：
F. Dellaert
机器人学中的许多估计、规划和最优控制问题的核心都是优化问题。在大多数这样的优化问题中，要最大化或最小化的目标是由许多不同的因素或项组成的，这些因素或项本质上是局部的-也就是说，它们只依赖于变量的一小部分。对这种局部性结构建模的一种特别有见地的方法是使用因子图的概念，这是一种二部图模型，其中因子表示变量子集上的函数。因子图可以表示机器人中的各种问题，提供提高计算性能的机会，并有助于设计和思考如何对问题建模，即使是在性能考虑之外。我详细讨论了这三个方面的每一个方面，并回顾了几个最先进的机器人应用，在这些应用中，因子图已经获得了巨大的成功。
Many estimation, planning, and optimal control problems in robotics have an optimization problem at their core. In most of these optimization problems, the objective to be maximized or minimized is composed of many different factors or terms that are local in nature—that is, they depend only on a small subset of the variables. A particularly insightful way of modeling this locality structure is to use the concept of factor graphs, a bipartite graphical model in which factors represent functions on subsets of variables. Factor graphs can represent a wide variety of problems across robotics, expose opportunities to improve computational performance, and are beneficial in designing and thinking about how to model a problem, even aside from performance considerations. I discuss each of these three aspects in detail and review several state-of-the-art robotics applications in which factor graphs have been used with great success.