Rethinking the semantic classification of indoor places by mobile robots

TL;DR

This paper proposes a new paradigm for semantic classification of indoor spaces by service robots, allowing intra-room label confusions to improve object search capabilities.

Contribution

It introduces a relaxed labeling approach that tolerates confusions within rooms, enhancing the robot's adaptability and usefulness in dynamic environments.

Findings

Confusions within room labels can be beneficial for robot tasks.

The approach improves object search performance.

A proof of concept demonstrates the paradigm's potential.

Abstract

A significant challenge in service robots is the semantic understanding of their surrounding areas. Traditional approaches addressed this problem by segmenting the floor plan 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 semantic classifiers by allowing confusions inside rooms. Our hypothesis is that those confusions can be beneficial to a service robot. We present a proof of concept in the task of searching for objects.