RT Conference Proceedings
T1 Joint Categorization of Objects and Rooms for Mobile Robots.
A1 Ruiz-Sarmiento, José Raúl
A1 Galindo-Andrades, Cipriano
A1 González-Jiménez, Antonio Javier
K1 Robots móviles - Diseño y construcción
AB In general, the problems of objects' and rooms' categorizations for robotic applications have been addressed separately. The current trend is, however, towards a joint modelling of both issues in order to leverage their mutual contextual relations: object → room (e.g. the detection of a microwave indicates that the room is likely to be a kitchen), and room → object (e.g. if the robot is in a bathroom, it is probable to find a toilet). Probabilistic Graphical Models (PGMs) are typically employed to conveniently cope with such relations, relying on inference processes to hypothesize about objects' and rooms' categories. In this work we present a Conditional Random Field (CRF) model, a particular type of PGM, to jointly categorize objects and rooms from RGBD images exploiting object-object and object-room relations. The learning phase of the proposed CRF uses Human Knowledge (HK) to eliminate the necessity of gathering real training data. Concretely, HK is acquired through elicitation and codified into an ontology, which is exploited to effortless generate an arbitrary number of representative synthetic samples for training. The performance of the proposed CRF model has been assessed using the NYU2 dataset, achieving a success of ~ 70% categorizing both, objects and rooms.
PB IEEE
YR 2015
FD 2015
LK https://hdl.handle.net/10630/36399
UL https://hdl.handle.net/10630/36399
LA eng
NO Ruiz-Sarmiento, J. R., Galindo, C., & González-Jiménez, J. (2015, September). Joint categorization of objects and rooms for mobile robots. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2523-2528). IEEE.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026