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.

翻译：服务机器人面临的一个重大挑战是对其周围环境的语义理解。传统方法通过将平面图分割为对应于完整房间的区域来解决这一问题，并为这些区域分配符合人类感知的标签，例如办公室或厨房。然而，同一房间内的不同区域可能以不同方式被使用：我厨房里的桌子和椅子能否成为我的办公室？该区域现在属于什么类别？办公室还是厨房？为了适应这些情况，我们提出一种新范式，即通过允许房间内部的混淆，有意放松语义分类器的最终标注结果。我们的假设是，这些混淆可能对服务机器人有益。我们在物体搜索任务中提供了一个概念验证。