In this work, we deal with classification of mobile robot locations into semantic categories such as room, corridor, and doorway using 2D laser data. Previous studies were generally able to distinguish room and corridor classes; however, the classification of doorway locations has not been satisfactory. To increase the classification accuracy of doorway class, we proposed a new point-based deep learning architecture, namely 2DLaserNet. In contrast to the well-known point-based deep learning techniques, 2DLaserNet exploits the ordered relation between successive points in the point cloud generated from 2D laser readings. In this way, 2DLaserNet is able to learn the geometric characteristics of laser scans corresponding to room, corridor, and doorway classes. We used the publicly available Freiburg 79 dataset to validate the effectiveness of the proposed approach, especially for the doorway class. Besides, we incorporated synthetic data to account for the intra-class variety for doorway locations. We also conducted experiments on the Freiburg 52 test dataset to examine the generalization ability of the proposed architecture trained with the Freiburg 79 dataset. We observed that 2DLaserNet outperforms state-of-the-art methods and well-known point-based deep learning techniques for doorway class.

在这项工作中，我们使用 2D 激光数据将移动机器人位置分类为语义类别，例如房间、走廊和门口。以前的研究通常能够区分房间和走廊类别；然而，门口位置的分类并不令人满意。为了提高门类的分类精度，我们提出了一种新的基于点的深度学习架构，即 2DLaserNet。与众所周知的基于点的深度学习技术相比，2DLaserNet 利用了从 2D 激光读数生成的点云中连续点之间的有序关系。通过这种方式，2DLaserNet 能够学习对应于房间、走廊和门口类别的激光扫描的几何特征。我们使用公开可用的 Freiburg 79 数据集来验证所提出方法的有效性，尤其是对于门口类。此外，我们结合了合成数据来解释门口位置的类内多样性。我们还在 Freiburg 52 测试数据集上进行了实验，以检查使用 Freiburg 79 数据集训练的拟议架构的泛化能力。我们观察到 2DLaserNet 优于最先进的方法和众所周知的基于点的门口类深度学习技术。我们还在 Freiburg 52 测试数据集上进行了实验，以检查使用 Freiburg 79 数据集训练的拟议架构的泛化能力。我们观察到 2DLaserNet 优于最先进的方法和众所周知的基于点的门口类深度学习技术。我们还在 Freiburg 52 测试数据集上进行了实验，以检查使用 Freiburg 79 数据集训练的拟议架构的泛化能力。我们观察到 2DLaserNet 优于最先进的方法和众所周知的基于点的门口类深度学习技术。