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 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 supervision for the learning. 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. Our
algorithms outperform previous work that uses hand-crafted features and Support
Vector Regression (SVR)[19].

DeepNav 是一个基于卷积神经网络（CNN）的算法，使用城市街景图像实现导航；使用 10 个城市图形和超过 100 万个街景图像组织的数据集，提出了 3 种监督学习方法，并表明可以使用 A * 搜索在城市图中生成学习的监督。