DeepNav: Learning to Navigate Large Cities

Samarth Brahmbhatt, James Hays; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5193-5202

We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for navigating large cities using locally visible street-view images. The DeepNav agent learns to reach its destination quickly by making the correct navigation decisions at intersections. We collect a large-scale dataset of street-view images organized in a graph where nodes are connected by roads. This dataset contains 10 city graphs and a total of more than 1 million street-view images. We propose 3 supervised learning approaches for the navigation task, and show how A* search in the city graph can be used to generate labels for the images. Our annotation process is fully automated using publicly available mapping services, and requires no human input. We evaluate the proposed DeepNav models on 4 held-out cities for navigating to 5 different types of destinations and show that our algorithms outperform previous work that uses hand-crafted features and Support Vector Regression (SVR).