2026-05-30T07:51:27Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/120262026-02-03T11:08:49Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Ruiz-Sarmiento, José Raúl
author
Galindo-Andrades, Cipriano
author
González-Jiménez, Antonio Javier
2016-09-15T11:35:41Z
2016-09-15T11:35:41Z
2016
http://hdl.handle.net/10630/12026
The suitable operation of mobile robots when providing Ambient Assisted Living (AAL) services calls for robust object recognition capabilities. Probabilistic Graphical Models (PGMs) have become the de-facto choice in recognition systems aiming to e ciently exploit contextual relations among objects, also dealing with the uncertainty inherent to the robot workspace. However, these models can perform in an inco herent way when operating in a long-term fashion out of the laboratory, e.g. while recognizing objects in peculiar con gurations or belonging to new types. In this work we propose a recognition system that resorts to PGMs and common-sense knowledge, represented in the form of an ontology, to detect those inconsistencies and learn from them. The utilization of the ontology carries additional advantages, e.g. the possibility to verbalize the robot's knowledge. A primary demonstration of the system capabilities has been carried out with very promising results.
eng
by-nc-nd
Robots autónomos
Probability and Common-Sense: Tandem Towards Robust Robotic Object Recognition in Ambient Assisted Living
journal article
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URL
https://riuma.uma.es/bitstreams/09247a0e-324d-4f98-91ae-759369b3f7a4/download
File
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9199b9a891d0886a091cc2f547de29ee
661109
application/pdf
UCAmi16-Probability_and_Common_Sense__Tandem_Towards_Robust_Robotic_Object_Recognition_in_Ambient_Assisted_Living.pdf