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arXiv 1301.0551cs.LGcs.ROstat.ML
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移动机器人学习非静止环境的层次目标地图

Learning Hierarchical Object Maps Of Non-Stationary Environments with mobile robots

Stanford University（斯坦福大学）；Carnegie Mellon University（卡内基梅隆大学）

Dragomir Anguelov, Rahul Biswas, Daphne Koller, Benson Limketkai, Sebastian Thrun

备注：
Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002)
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摘要：

构建机器人环境的模型或地图是一个非常活跃的研究领域;然而，大多数现有技术构建非结构化地图并假设静态环境。在本文中，我们提出了一种算法，学习对象模型的非静止对象中发现的办公室型环境。我们的算法利用了这样一个事实，即在办公室环境中发现的许多对象看起来都很相似（例如，椅子，回收箱）。它通过两级分层表示来实现这一点，该表示将各个对象与对象类的通用形状模板链接起来。我们推导出一个近似EM算法学习形状参数的层次结构的两个级别，使用局部占用网格图表示形状。此外，我们开发了一个贝叶斯模型选择算法，使机器人能够估计环境中的对象和对象模板的总数。使用一个真正的机器人配备了激光测距仪的实验结果表明，我们的方法在学习简单的办公环境中基于对象的地图表现良好。该方法优于以前开发的非分层算法，模型对象，但缺乏类模板。

英文摘要：

Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exploits the fact that many objects found in office environments look alike (e.g., chairs, recycling bins). It does so through a two-level hierarchical representation, which links individual objects with generic shape templates of object classes. We derive an approximate EM algorithm for learning shape parameters at both levels of the hierarchy, using local occupancy grid maps for representing shape. Additionally, we develop a Bayesian model selection algorithm that enables the robot to estimate the total number of objects and object templates in the environment. Experimental results using a real robot equipped with a laser range finder indicate that our approach performs well at learning object-based maps of simple office environments. The approach outperforms a previously developed non-hierarchical algorithm that models objects but lacks class templates.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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