As one of the most promising areas, mobile robots draw much attention these years. Current work in this field is often evaluated in a few manually designed scenarios, due to the lack of a common experimental platform. Meanwhile, with the recent development of deep learning techniques, some researchers attempt to apply learning-based methods to mobile robot tasks, which requires a substantial amount of data. To satisfy the underlying demand, in this paper we build HouseExpo, a large-scale indoor layout dataset containing 35,357 2D floor plans including 252,550 rooms in total. Together we develop Pseudo-SLAM, a lightweight and efficient simulation platform to accelerate the data generation procedure, thereby speeding up the training process. In our experiments, we build models to tackle obstacle avoidance and autonomous exploration from a learning perspective in simulation as well as real-world experiments to verify the effectiveness of our simulator and dataset. All the data and codes are available online and we hope HouseExpo and Pseudo-SLAM can feed the need for data and benefits the whole community.
翻译:作为最有希望的领域之一,流动机器人在这些年引起了很大的注意。由于缺乏共同的实验平台,目前这一领域的工作往往在几个人工设计的情景中进行评估。与此同时,随着最近深层次学习技术的发展,一些研究人员试图将基于学习的方法应用于移动机器人的任务,这需要大量的数据。为了满足基本需求,我们在本文件中建造了HouseExpto,一个大型室内布局数据集,包含35 3557 2D层计划,总共包括252 550个房间。我们一起开发了Pseudo-SLAM,这是一个轻量和高效的模拟平台,以加速数据生成程序,从而加快培训进程。在我们的实验中,我们建立了模型,从模拟的学习角度以及真实世界实验的角度解决障碍的避免和自主探索问题,以核实我们的模拟器和数据集的有效性。所有数据和代码都在线提供,我们希望HousExplo和Pseudo-SLAM能够满足数据的需求,造福整个社区。