Abstract
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.
Abstract (translated)
移动机器人作为最有前途的领域之一，近年来备受关注。由于缺乏通用的实验平台，该领域的当前工作通常在一些手动设计的场景中进行评估。同时，随着深度学习技术的不断发展，一些研究者试图将基于学习的方法应用到移动机器人任务中，这需要大量的数据。为了满足潜在的需求，本文构建了一个包含35357个二维平面布置图的大型室内布局数据集，共252550个房间。我们共同开发了一个轻量级和高效的仿真平台伪SLAM，以加速数据生成过程，从而加快训练过程。在我们的实验中，我们从学习的角度在模拟和现实实验中建立模型来解决障碍回避和自主探索问题，以验证模拟器和数据集的有效性。所有的数据和代码都可以在线获取，我们希望世博会和伪SLAM能够满足对数据的需求，并使整个社区受益。
URL
https://arxiv.org/abs/1903.09845
