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摘要翻译：
定位，即根据传感器数据估计机器人的位置，是移动机器人学中的一个基本问题。本文提出了一种适用于动态环境的马尔可夫定位算法，该算法能够提供精确的位置估计。马尔可夫定位的核心思想是在机器人所处环境的所有位置的空间上保持一个概率密度。我们的方法以度量的方式表示这个空间，使用细粒度网格来近似密度。它能够从零开始对机器人进行全局定位，并从定位失败中恢复。它对环境的近似模型（如占用网格地图）和噪声传感器（如超声波传感器）具有鲁棒性。我们的方法还包括一种过滤技术，该技术允许移动机器人即使在人口稠密的环境中也能可靠地估计其位置，在这种环境中，人群会长时间地阻塞机器人的传感器。本文所描述的方法已经在移动机器人的几个实际应用中得到了实现和测试，包括两个移动机器人作为交互式博物馆导游的部署。
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英文标题：
《Markov Localization for Mobile Robots in Dynamic Environments》
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作者：
W. Burgard, D. Fox, S. Thrun
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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：
Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described here has been implemented and tested in several real-world applications of mobile robots, including the deployments of two mobile robots as interactive museum tour-guides.
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PDF链接：
https://arxiv.org/pdf/1106.0222
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