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Sensors 2017, 17(2), 353; doi:10.3390/s17020353

A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots

1
Robotics and Artificial Vision Laboratory, University of Extremadura, 10003 Cáceres, Spain
2
Robotics Research Center, IIIT Hyderabad, 500032 Hyderabad, India
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 21 December 2016 / Revised: 30 January 2017 / Accepted: 8 February 2017 / Published: 11 February 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [4153 KB, uploaded 11 February 2017]   |  

Abstract

Object detection and classification have countless applications in human–robot interacting systems. It is a necessary skill for autonomous robots that perform tasks in household scenarios. Despite the great advances in deep learning and computer vision, social robots performing non-trivial tasks usually spend most of their time finding and modeling objects. Working in real scenarios means dealing with constant environment changes and relatively low-quality sensor data due to the distance at which objects are often found. Ambient intelligence systems equipped with different sensors can also benefit from the ability to find objects, enabling them to inform humans about their location. For these applications to succeed, systems need to detect the objects that may potentially contain other objects, working with relatively low-resolution sensor data. A passive learning architecture for sensors has been designed in order to take advantage of multimodal information, obtained using an RGB-D camera and trained semantic language models. The main contribution of the architecture lies in the improvement of the performance of the sensor under conditions of low resolution and high light variations using a combination of image labeling and word semantics. The tests performed on each of the stages of the architecture compare this solution with current research labeling techniques for the application of an autonomous social robot working in an apartment. The results obtained demonstrate that the proposed sensor architecture outperforms state-of-the-art approaches. View Full-Text
Keywords: robot sensors; ambient intelligence sensors; deep learning; object detection; object recognition; word semantics robot sensors; ambient intelligence sensors; deep learning; object detection; object recognition; word semantics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Gutiérrez, M.A.; Manso, L.J.; Pandya, H.; Núñez, P. A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots. Sensors 2017, 17, 353.

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