TY  - CONF
ID  - davfercha_APPIS_2020_room_categorization
T1  - From Object Detection to Room Categorization in Robotics
A1  - Fernandez-Chaves, David
A1  - Ruiz-Sarmiento, J. R.
A1  - Petkov, Nicolai
A1  - Gonzalez-Jimenez, Javier
JA  - International Conference on Applications of Intelligent Systems (APPIS)
Y1  - 2020
CY  - Las Palmas de Gran Canaria (Spain)
UR  - https://drive.google.com/uc?id=1pUd5L1to0M4TkzPlISQzXgV2z8OaEu89&export=download&authuser=0
M2  - doi: https://doi.org/10.1145/3378184.3378230
KW  - BayesianInference
KW  - mobile robots
KW  - object recognition
KW  - Ontologies
KW  - Room Categorization
KW  - Semantic Knowledge
KW  - uncertainty propagation
N2  - This article deals with the problem of room categorization, i.e. the classification of a room as being a bathroom, kitchen, living-room, bedroom, etc., by an autonomous robot operating in home environments. For that, we propose a room categorization system based on a Bayesian probabilistic framework that combines object detections and its semantics. For detecting objects we resort to a state-of-the-art CNN, Mask R-CNN, while the meaning or semantics of those detections is provided by an ontology. Such an ontology encodes the relations between object and room categories, that is, in which room types the different object categories are typically found (toilets in bathrooms, microwaves in kitchens, etc.). The Bayesian framework is in charge of fusing both sources of information and providing a probability distribution over the set of categories the room can belong to. The proposed system has been evaluated in houses from the Robot@Home dataset, validating its effectiveness under real-world conditions.
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