Exploiting the confusions of semantic places to improve service robotic tasks in indoor environments

A significant challenge in service robots is the semantic understanding of their surrounding areas. Traditional approaches addressed this problem by segmenting the environment into regions corresponding to full rooms that are assigned labels consistent with human perception, e.g. office or kitchen. However, different areas inside the same room can be used in different ways: Could the table and the chair in my kitchen become my office ? What is the category of that area now? office or kitchen? To adapt to these circumstances we propose a new paradigm where we intentionally relax the resulting labeling of place classifiers by allowing confusions, and by avoiding further filtering leading to clean full room classifications. Our hypothesis is that confusions can be beneficial to a service robot and, therefore, they can be kept and better exploited. Our approach creates a subdivision of the environment into different regions by maintaining the confusions which are due to the scene appearance or to the distribution of objects. In this paper, we present a proof of concept implemented in simulated and real scenarios, that improves efficiency in the robotic task of searching for objects by exploiting the confusions in place classifications.