SREF: Semantics-Refined Feature Extraction for Long-Term Visual Localization

Area of Science:

• Computer Vision
• Robotics
• Artificial Intelligence

Background:

• Visual localization is crucial for autonomous systems but challenged by environmental variations.
• Existing methods struggle with illumination changes, viewpoint shifts, and dynamic objects.

Purpose of the Study:

• To develop a fine-grained semantics-guided feature extraction framework for robust visual localization.
• To improve feature stability and suppress dynamic disturbances in changing environments.

Main Methods:

• A fine-grained semantic refinement module categorizes scenes into stability-homogeneous sub-classes.
• A dual-attention mechanism improves feature repeatability and semantic consistency.
• Integration of physical priors and self-supervised clustering for learning reliable features.

Main Results:

• Achieved state-of-the-art accuracy and robustness on Aachen and RobotCar-Seasons benchmarks.
• Demonstrated strong localization performance under challenging day/night and seasonal conditions.
• Maintained real-time efficiency, bridging semantic guidance with stability estimation.

Conclusions:

• The proposed framework effectively enhances visual localization accuracy and robustness.
• It offers a significant advancement in handling environmental variability for autonomous driving and robotics.
• The method provides a reliable approach for feature representation in dynamic scenarios.