DK-SLAM: Monocular Visual SLAM with Deep Keypoints Adaptive Learning,
  Tracking and Loop-Closing
Hao Qu Lilian Zhang Jun Mao Junbo Tie Xiaofeng He Xiaoping Hu Yifei Shi Changhao Chen
Unreliable feature extraction and matching in handcrafted features undermine
the performance of visual SLAM in complex real-world scenarios. While learned
local features, leveraging CNNs, demonstrate proficiency in capturing
high-level information and excel in matching benchmarks, they encounter
challenges in continuous motion scenes, resulting in poor generalization and
impacting loop detection accuracy. To address these issues, we present DK-SLAM,
a monocular visual SLAM system with adaptive deep local features. MAML
optimizes the training of these features, and we introduce a coarse-to-fine
feature tracking approach. Initially, a direct method approximates the relative
pose between consecutive frames, followed by a feature matching method for
refined pose estimation. To counter cumulative positioning errors, a novel
online learning binary feature-based online loop closure module identifies loop
nodes within a sequence. Experimental results underscore DK-SLAM's efficacy,
outperforms representative SLAM solutions, such as ORB-SLAM3 on publicly
available datasets.