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Deep reinforcement learning (DRL) demonstrates its potential in learning amodel-free navigation policy for robot visual navigation. However, thedata-demanding algorithm relies on a large number of navigation trajectories intraining. Existing datasets supporting training such robot navigationalgorithms consist of either 3D synthetic scenes or reconstructed scenes.Synthetic data suffers from domain gap to the real-world scenes while visualinputs rendered from 3D reconstructed scenes have undesired holes andartifacts. In this paper, we present a new dataset collected in real-world tofacilitate the research in DRL based visual navigation. Our dataset includes 3Dreconstruction for real-world scenes as well as densely captured real 2D imagesfrom the scenes. It provides high-quality visual inputs with real-world scenecomplexity to the robot at dense grid locations. We further study and benchmarkone recent DRL based navigation algorithm and present our attempts and thoughtson improving its generalizability to unseen test targets in the scenes.

The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation