Applying Probabilistic Mixture Models to Semantic Place Classification in Mobile Robotics

Cristiano Premebida, Diego R. Faria, Francisco A. de Souza, Urbano Nunes

Research output: Chapter in Book/Published conference outputConference publication


n this paper a study is made of the problem of classifying scenarios, in terms of semantic categories, based on data gathered from sensors mounted on-board mobile robots operating indoors. Once the data are transformed to feature space, supervised classification is performed by a probabilistic approach called Dynamic Bayesian Mixture Models (DBMM). This approach combines class-conditional probabilities from supervised learning models and incorporates past inferences. In this work, several experiments on multi-class semantic place classification are reported based on publicly available datasets. Such experiments were conducted in a such way that generalization aspects are emphasized, which is particularly important in real-world applications. Benchmark results show the effectiveness and competitive performance of the DBMM method, in terms of classification rates, using features extracted from 2D range data and from a RGB-D (Kinect) sensor.
Original languageEnglish
Title of host publicationIEEE/RSJ IEEE IROS'15: International Conference on Intelligent Robots and Systems
Number of pages6
Publication statusPublished - 2015


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