Semantic mapping employs explicit labels to deal with sensor data in robotic mapping processes. In this paper we present a method for boosting performance of spatial mapping, through the use of a probabilistic ontology, expressed with a probabilistic description logic. Reasoning with this ontology allows segmentation and tagging of sensor data acquired by a robot during navigation; hence a robot can construct metric maps topologically. We report experiments with a real robot to validate our approach, thus moving closer to the goal of integrating mapping and semantic labeling processes. © 2010 Springer-Verlag.
CITATION STYLE
Polastro, R., Corrêa, F., Cozman, F., & Okamoto, J. (2010). Semantic mapping with a probabilistic description logic. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6404 LNAI, pp. 62–71). https://doi.org/10.1007/978-3-642-16138-4_7
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