Topometric Localization with Deep Learning

Abstract: Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective however their accuracy and reliability is typically inferior to LiDAR-based methods. In this work, we propose a vision-based localization approach that learns from LiDAR-based localization methods by using their output as training data, thus combining a cheap, passive sensor with an ac- curacy that is on-par with LiDAR-based localization. The approach consists of two deep networks trained on visual odometry and topological localization, respectively, and a successive optimization to combine the predictions of these two networks. Fur- thermore, we introduce a new challenging pedestrian-based dataset for localization with a high degree of noise. Results obtained by evaluating the proposed approach on this novel dataset demonstrate localization errors up to 10 times smaller than those obtained with traditional vision-based localization methods.