  Many works in the recent literature introduce semantic mapping methods that
use CNNs (Convolutional Neural Networks) to recognize semantic properties in
images. The types of properties (eg.: room size, place category, and objects)
and their classes (eg.: kitchen and bathroom, for place category) are usually
predefined and restricted to a specific task. Thus, all the visual data
acquired and processed during the construction of the maps are lost and only
the recognized semantic properties remain on the maps. In contrast, this work
introduces a topological semantic mapping method that uses deep visual features
extracted by a CNN (GoogLeNet), from 2D images captured in multiple views of
the environment as the robot operates, to create, through averages,
consolidated representations of the visual features acquired in the regions
covered by each topological node. These representations allow flexible
recognition of semantic properties of the regions and use in other visual
tasks. Experiments with a real-world indoor dataset showed that the method is
able to consolidate the visual features of regions and use them to recognize
objects and place categories as semantic properties, and to indicate the
topological location of images, with very promising results.
