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.
|Title of host publication||IEEE/RSJ IEEE IROS'15: International Conference on Intelligent Robots and Systems|
|Number of pages||6|
|Publication status||Published - 2015|
Premebida, C., Faria, D. R., Souza, F. A. D., & Nunes, U. (2015). Applying Probabilistic Mixture Models to Semantic Place Classification in Mobile Robotics. In IEEE/RSJ IEEE IROS'15: International Conference on Intelligent Robots and Systems (pp. 4265-4270) https://doi.org/10.1109/IROS.2015.7353981