TY  - JOUR
T1  - Building Multiversal Semantic Maps for Mobile Robot Operation
A1  - Ruiz-Sarmiento, J. R.
A1  - Galindo, Cipriano
A1  - Gonzalez-Jimenez, Javier
JA  - Knowledege-Based Systems
Y1  - 2017
VL  - 119
SP  - 257
EP  - 272
M2  - doi: 10.1016/j.knosys.2016.12.016
N2  - Semantic maps augment metric-topological maps with meta-information, i.e. l semantic knowledge aimed at the planning and execution of high-level robotic tasks. Semantic knowledge typically encodes human-like concepts, like types of objects and rooms, which are connected to sensory data when symbolic representations of percepts from the robot workspace are grounded to those concepts. Such a symbol grounding is usually carried out by algorithms that individually categorize each symbol and provide a crispy outcome – a symbol is either a member of a category or not. Such approach is valid for a variety of tasks, but it fails at: (i) dealing with the uncertainty inherent to the grounding process, and (ii) jointly exploiting the contextual relations among concepts (e.g. microwaves are usually in kitchens). This work provides a solution for probabilistic symbol grounding that overcomes these limitations. Concretely, we rely on Conditional Random Fields (CRFs) to model and exploit contextual relations, and to provide measurements about the uncertainty coming from the possible groundings in the form of beliefs (e.g. an object can be categorized (grounded) as a microwave or as a nightstand with beliefs 0.6 and 0.4, respectively). Our solution is integrated into a novel semantic map representation called Multiversal Semantic Map   (MvSmap), which keeps the sets of different groundings, or universes, as instances of ontologies annotated with the obtained beliefs for their posterior exploitation. The suitability of our proposal has been proven with the Robot@Home dataset, a repository that contains challenging multi-modal sensory information gathered by a mobile robot in home environments.
M1  - img_url=http%3A%2F%2Fmapir.isa.uma.es%2Fimagesrepo%2Fpapers%2F2017%2F2017_raul_kbs.png
M1  - rank_indexname=JCR-2017
M1  - rank_pos_in_category=14
M1  - rank_num_in_category=132
M1  - rank_cat_name=COMPUTER%20SCIENCE%2C%20ARTIFICIAL%20INTELLIGENCE
M1  - impact_factor=4.396
ER  -