Previous learning-based methods that rely on 2D laser data to classify indoor robot locations into semantic classes were successful in distinguishing between rooms and corridors. However, the classification accuracy remained low for doorway locations. We propose a semantic place classification method that uses a rule-based doorway detection algorithm followed by a classification scheme that models training data through either K-means clustering or learning vector quantization. We conducted extensive experiments on the Freiburg 79 dataset and compared our method to previous semantic place classification algorithms. The doorway detection algorithm we propose significantly increases the classification accuracy for doorway locations as compared to the state-of-the-art performance. We applied our method, trained on the Freiburg 79 dataset, to Freiburg 52 and ESOGU datasets in order to demonstrate its generalization ability.

中文翻译：

---

通过2D激光扫描对移动机器人位置进行语义分类

先前依靠2D激光数据将室内机器人位置分类为语义类的基于学习的方法成功地区分了房间和走廊。但是，对于门口位置，分类精度仍然很低。我们提出了一种语义场所分类方法，该方法使用基于规则的门口检测算法，然后使用通过K-表示聚类或学习矢量量化。我们对Freiburg 79数据集进行了广泛的实验，并将我们的方法与以前的语义场所分类算法进行了比较。与最新技术相比，我们提出的门口检测算法大大提高了门口位置的分类精度。我们将经过弗莱堡79数据集训练的方法应用于弗莱堡52和ESOGU数据集，以证明其泛化能力。