Building Multiversal Semantic Maps for Mobile Robot Operation
Draft Version. Final version published in Knowledge-Based Systems Building Multiversal Semantic Maps for Mobile Robot Operation Jose-Raul Ruiz-Sarmientoa,∗, Cipriano Galindoa, Javier Gonzalez-Jimeneza aMachine Perception and Intelligent Robotics Group System Engineering and Auto. Dept., Instituto de Investigaci´onBiom´edicade M´alaga (IBIMA), University of M´alaga, Campus de Teatinos, 29071, M´alaga, Spain. Abstract Semantic maps augment metric-topological maps with meta-information, i.e. 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. This 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 different groundings, or universes, as instances of ontologies annotated with the obtained beliefs for their posterior exploitation.
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