  The performance of visual SLAM in complex, real-world scenarios is often
compromised by unreliable feature extraction and matching when using
handcrafted features. Although deep learning-based local features excel at
capturing high-level information and perform well on matching benchmarks, they
struggle with generalization in continuous motion scenes, adversely affecting
loop detection accuracy. Our system employs a Model-Agnostic Meta-Learning
(MAML) strategy to optimize the training of keypoint extraction networks,
enhancing their adaptability to diverse environments. Additionally, we
introduce a coarse-to-fine feature tracking mechanism for learned keypoints. It
begins with a direct method to approximate the relative pose between
consecutive frames, followed by a feature matching method for refined pose
estimation. To mitigate cumulative positioning errors, DK-SLAM incorporates a
novel online learning module that utilizes binary features for loop closure
detection. This module dynamically identifies loop nodes within a sequence,
ensuring accurate and efficient localization. Experimental evaluations on
publicly available datasets demonstrate that DK-SLAM outperforms leading
traditional and learning based SLAM systems, such as ORB-SLAM3 and LIFT-SLAM.
These results underscore the efficacy and robustness of our DK-SLAM in varied
and challenging real-world environments.
