Training Semantic Descriptors for Image-Based Localization
This addresses robust localization for autonomous vehicles in varying environmental conditions, though it is incremental as it adapts existing image retrieval approaches.
The paper tackled vehicle localization by using descriptors from semantically segmented images, achieving performance comparable to state-of-the-art RGB-based methods, especially under challenging illumination and seasonal changes.
Vision based solutions for the localization of vehicles have become popular recently. We employ an image retrieval based visual localization approach. The database images are kept with GPS coordinates and the location of the retrieved database image serves as an approximate position of the query image. We show that localization can be performed via descriptors solely extracted from semantically segmented images. It is reliable especially when the environment is subjected to severe illumination and seasonal changes. Our experiments reveal that the localization performance of a semantic descriptor can increase up to the level of state-of-the-art RGB image based methods.