Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
This work addresses the challenge of outdoor navigation for blind and visually-impaired individuals, but it is incremental as it builds on existing RL methods with a new simulator.
The paper tackles the problem of creating navigation assistants for blind and visually-impaired people by introducing SEVN, a sidewalk simulator with labeled panoramic images and tasks, and tests a PPO-based RL agent that fuses multi-modal data to navigate to goal doors, achieving results that provide a foundation for further research.
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.