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A Semantic Labeling of the Environment Based on What People Do

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URI: https://hdl.handle.net/10016/27880
ISSN: 1424-8220
DOI: https://doi.org/10.3390/s17020260
UXXI: AR/0000019674
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Semantic_sensors_2017.pdf (816.41 KB)
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2017-01-29
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MDPI
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Abstract
In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sound, number of people in a room and amount of movement of those people are items to be considered when trying to tell if people are doing different actions. These data are sampled, and it is assumed that people behave differently and perform different actions. A support vector machine is trained with the obtained samples, and therefore, it allows one to identify the room. Finally, the results are discussed and support the hypothesis that the proposed system can help to semantically label a room.
The research leading to these results has received funding from the RoboCity2030-III-CMproject (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+Den la Comunidad de Madrid and cofunded by Structural Funds of the EU and NAVEGASEAUTOCOGNAVproject (DPI2014-53525-C3-3-R), funded by Ministerio de Economía y Competitividad of Spain.
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Semantic labeling, Semantic navigation, Mobile robotics, Detecting people, Environment classification, range data, Robot, Classification, Recognition, Places
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Crespo,J., Gómez,C., Hernández,A., Barber,R. (2017). A Semantic Labeling of the Environment Based on What People Do. Sensors, 17 (2), 260.
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