Dynamic Bayesian Networks for semantic localization in robotics

Abstract

This project presents a solution based on Bayesian Artificial Intelligent for the problem of semantic localization in Autonomous Robots. We have developed a methodology that covers the following steps: (1) Image processing and discretization for creating feature-based scene descriptors. (2) Learning of static Bayesian Networks and Naive Bayes classifier. (3) Learning of Dynamical Bayesian Networks. (4) Evaluation of the models. (5) Comparison. We must pay attention to DBNs, which have proven to be a solution to consider. This process includes the use of different software tools, since it is not possible to cover all these fields with only one. That implies a great effort, because we must first know all the tools in order to solve the problem. Moreover, we have to implement our own techniques for the task of tool integration, as well as, a discretization process for histograms and a method of constructing DBNs. All this process has been tested in a real case: the KTH-IDOL2 (Image Database for rObot Localization) dataset for scene classification. Our experimental results show that BN models obtain good accuracy values.