Learning Semantic Maps from Natural Language Descriptions
Learning Semantic Maps from Natural Language Descriptions
复制标题
从自然语言描述中学习语义图
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
S. Teller
中科院分区：
文献类型：
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作者：
Matthew R. Walter;Sachithra Hemachandra;Bianca Homberg;Stefanie Tellex;S. Teller
本文提出了一种算法，该算法使机器人能够从自然语言描述中有效地学习其环境的以人为中心的模型。请勿使用此信息来改善我们的算法的新颖性。机器人的低水平传感器流共同估计了环境的混合度量，拓扑和语义表示。低级传感器的度量观察有效地根据自然语言和低级的语义图进行了分布传感器信息。
This paper proposes an algorithm that enables robots to efficiently learn human-centric models of their environment from natural language descriptions. Typical semantic mapping approaches augment metric maps with higher-level properties of the robot’s surroundings (e.g., place type, object locations), but do not use this information to improve the metric map. The novelty of our algorithm lies in fusing high-level knowledge, conveyed by speech, with metric information from the robot’s low-level sensor streams. Our method jointly estimates a hybrid metric, topological, and semantic representation of the environment. This semantic graph provides a common framework in which we integrate concepts from natural language descriptions (e.g., labels and spatial relations) with metric observations from low-level sensors. Our algorithm efficiently maintains a factored distribution over semantic graphs based upon the stream of natural language and low-level sensor information. We evaluate the algorithm’s performance and demonstrate that the incorporation of information from natural language increases the metric, topological and semantic accuracy of the recovered environment model.