摘要翻译：
隐马尔可夫模型和部分可观测马尔可夫决策过程为动态系统建模提供了有用的工具。它们特别适用于表示环境的拓扑结构，如公路网和办公楼，这是机器人导航和规划的典型。本文描述了一个形式化的框架，用于将容易获得的里程信息和几何约束合并到模型和学习它们的算法中。通过利用这些信息，学习HMMS/POMDPS可以产生更好的解决方案，需要更少的迭代，同时在面对数据缩减时具有鲁棒性。仿真和真实机器人数据的实验结果表明了该方法的有效性。
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英文标题：
《Learning Geometrically-Constrained Hidden Markov Models for Robot
  Navigation: Bridging the Topological-Geometrical Gap》
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作者：
L. P. Kaelbling, H. Shatkay
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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英文摘要：
  Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily available odometric information and geometrical constraints into both the models and the algorithm that learns them. By taking advantage of such information, learning HMMs/POMDPs can be made to generate better solutions and require fewer iterations, while being robust in the face of data reduction. Experimental results, obtained from both simulated and real robot data, demonstrate the effectiveness of the approach.
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PDF链接：
https://arxiv.org/pdf/1106.0680