  The semantic localization problem in robotics consists in determining the
place where a robot is located by means of semantic categories. The problem is
usually addressed as a supervised classification process, where input data
correspond to robot perceptions while classes to semantic categories, like
kitchen or corridor.
  In this paper we propose a framework, implemented in the PCL library, which
provides a set of valuable tools to easily develop and evaluate semantic
localization systems. The implementation includes the generation of 3D global
descriptors following a Bag-of-Words approach. This allows the generation of
dimensionality-fixed descriptors from any type of keypoint detector and feature
extractor combinations. The framework has been designed, structured and
implemented in order to be easily extended with different keypoint detectors,
feature extractors as well as classification models.
  The proposed framework has also been used to evaluate the performance of a
set of already implemented descriptors, when used as input for a specific
semantic localization system. The results obtained are discussed paying special
attention to the internal parameters of the BoW descriptor generation process.
Moreover, we also review the combination of some keypoint detectors with
different 3D descriptor generation techniques.
