HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots
This provides a dataset and simulator for the mobile robotics community, addressing data scarcity for learning-based methods, but it is incremental as it builds on existing needs without introducing a new paradigm.
The authors tackled the lack of a common experimental platform and data for learning-based algorithms on mobile robots by building HouseExpo, a large-scale indoor layout dataset with 35,126 2D floor plans and 252,550 rooms, and developed Pseudo-SLAM, a simulation platform to accelerate data generation and training.
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 35,126 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.