让机器人更灵活理解室内空间，不再死守房间标签
Rethinking the semantic classification of indoor places by mobile robots
- 允许同一房间内区域标签混淆，提升语义适应性
- 在找物任务中，混淆标签使机器人识别准确率提升12.3%
- 适合需要动态理解环境的服务机器人研究者
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。传统方法将平面图分割为对应完整房间的区域，并赋予与人类感知一致的标签，如办公室或厨房。然而，同一房间内的不同区域可能被用于不同用途：我厨房里的餐桌和椅子能否变成我的办公室？该区域现在属于办公室还是厨房？为应对这些情况，我们提出一种新范式，主动放宽语义分类器的标签结果，允许房间内部出现混淆。我们的假设是，这种混淆对服务机器人有益。我们在找物任务中进行了概念验证。
原文摘要 · Abstract (English)
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.
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