DK-SLAM: Monocular Visual SLAM with Deep Keypoints Adaptive Learning, Tracking and Loop-Closing
CoRR(2024)
摘要
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.
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