2018

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

Mo, Kaichun, Li, Haoxiang, Lin, Zhe et al.

Understand

Deep reinforcement learning (DRL) demonstrates its potential in learning a model-free navigation policy for robot visual navigation.

However, the data-demanding algorithm relies on a large number of navigation trajectories in training.
Existing datasets supporting training such robot navigation algorithms consist of either 3D synthetic scenes or reconstructed scenes.
Synthetic data suffers from domain gap to the real-world scenes while visual inputs rendered from 3D reconstructed scenes have undesired holes and artifacts.

Built on

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Then

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