SAMLoc: Structure-Aware Constraints With Multi-Task Distillation for Long-Term Visual Localization

Real-time and robust long-term visual localization is a key technology for autonomous driving. Season and illumination variance, as well as limited computing power make this problem more challenging. At present, most of the excellent visual localization algorithms cannot run in real-time on devices with limited computing power. In this paper, we propose SAMLoc, a self-supervised 6-DoF visual localization method with structure-aware and multi-task distillation. We integrate the structure-aware constraints into the hierarchical localization network of multi-task distillation, which greatly reduces the feature extraction time while ensuring localization accuracy, thus achieving real-time and robust large-scene localization on mobile devices. Our method takes both speed and accuracy into consideration, and extensive experiments have been conducted to validate the effectiveness of the proposed approach on several datasets. Our network is not only lightweight but also has excellent generalization ability, and still exhibits high localization accuracy even with challenging datasets.