Learning Semantic-Aware Local Features for Long Term Visual Localization.

Area of Science:

• Computer Vision
• Robotics
• Artificial Intelligence

Background:

• Long-term visual localization faces challenges due to appearance variations from illumination, seasonal changes, and human activities.
• Existing methods for local feature extraction often struggle with these severe appearance differences, impacting localization accuracy.
• Current deep learning approaches, while improved, still require extensive point correspondence annotations.

Purpose of the Study:

• To develop a novel method for learning semantic-aware local features to enhance the robustness of visual localization.
• To improve local feature matching by leveraging intrinsic semantic information invariant to appearance changes.
• To enhance the accuracy and speed of long-term visual localization systems.

Main Methods:

• Utilized a state-of-the-art CNN architecture (ASLFeat) for local feature learning.
• Integrated semantic information from an off-the-shelf semantic segmentation network to create semantic-aware feature maps.
• Merged correspondence-aware feature descriptors with semantic features to create enhanced final descriptors.

Main Results:

• Experiments demonstrated improved feature matching ability using the proposed semantic-aware local features.
• The learned semantic information effectively filtered noisy keypoints, leading to higher accuracy and faster matching.
• Significant improvements in localization accuracy were observed on benchmarks like Aachen Day and Night, Robotcar Seasons, and InLoc.

Conclusions:

• Semantic-aware local features offer a robust solution for long-term visual localization challenges.
• The integration of semantic information enhances feature descriptors and enables effective keypoint filtering.
• The proposed method shows competitive performance and encourages further research in semantic-driven feature learning for localization.