MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps
This work addresses the need for cost-effective and scalable localization in autonomous driving, offering a significant performance boost over existing methods.
The paper tackles the problem of real-time visual re-localization for autonomous driving using low-cost navigation maps instead of expensive HD maps, achieving nearly 10%/20% higher localization accuracy and 30/16 FPS improvements over the state-of-the-art on nuScenes and Argoverse datasets.
Robust localization is the cornerstone of autonomous driving, especially in challenging urban environments where GPS signals suffer from multipath errors. Traditional localization approaches rely on high-definition (HD) maps, which consist of precisely annotated landmarks. However, building HD map is expensive and challenging to scale up. Given these limitations, leveraging navigation maps has emerged as a promising low-cost alternative for localization. Current approaches based on navigation maps can achieve highly accurate localization, but their complex matching strategies lead to unacceptable inference latency that fails to meet the real-time demands. To address these limitations, we propose a novel transformer-based neural re-localization method. Inspired by image registration, our approach performs a coarse-to-fine neural feature registration between navigation map and visual bird's-eye view features. Our method significantly outperforms the current state-of-the-art OrienterNet on both the nuScenes and Argoverse datasets, which is nearly 10%/20% localization accuracy and 30/16 FPS improvement on single-view and surround-view input settings, separately. We highlight that our research presents an HD-map-free localization method for autonomous driving, offering cost-effective, reliable, and scalable performance in challenging driving environments.