  Millions of blind and visually-impaired (BVI) people navigate urban
environments every day, using smartphones for high-level path-planning and
white canes or guide dogs for local information. However, many BVI people still
struggle to travel to new places. In our endeavor to create a navigation
assistant for the BVI, we found that existing Reinforcement Learning (RL)
environments were unsuitable for the task. This work introduces SEVN, a
sidewalk simulation environment and a neural network-based approach to creating
a navigation agent. SEVN contains panoramic images with labels for house
numbers, doors, and street name signs, and formulations for several navigation
tasks. We study the performance of an RL algorithm (PPO) in this setting. Our
policy model fuses multi-modal observations in the form of variable resolution
images, visible text, and simulated GPS data to navigate to a goal door. We
hope that this dataset, simulator, and experimental results will provide a
foundation for further research into the creation of agents that can assist
members of the BVI community with outdoor navigation.
