Topological Semantic Mapping by Consolidation of Deep Visual Features

Abstract

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, the GoogLeNet, from 2D images captured in multiple views of the environment as the robot operates, to create consolidated representations of visual features acquired in the regions covered by each topological node. These consolidated representations allow flexible recognition of semantic properties of the regions and use in a range of visual tasks. The experiments, performed using 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. The objects are classified using the classification layer of GoogLeNet, without retraining, and the place categories are recognized using a shallow Multilayer Perceptron.