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As one of the most promising areas, mobile robots draw much attention theseyears. Current work in this field is often evaluated in a few manually designedscenarios, due to the lack of a common experimental platform. Meanwhile, withthe recent development of deep learning techniques, some researchers attempt toapply learning-based methods to mobile robot tasks, which requires asubstantial amount of data. To satisfy the underlying demand, in this paper webuild HouseExpo, a large-scale indoor layout dataset containing 35,126 2D floorplans including 252,550 rooms in total. Together we develop Pseudo-SLAM, alightweight and efficient simulation platform to accelerate the data generationprocedure, thereby speeding up the training process. In our experiments, webuild models to tackle obstacle avoidance and autonomous exploration from alearning perspective in simulation as well as real-world experiments to verifythe effectiveness of our simulator and dataset. All the data and codes areavailable online and we hope HouseExpo and Pseudo-SLAM can feed the need fordata and benefits the whole community.

HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots