DK-SLAM: Monocular Visual SLAM with Deep Keypoints Adaptive Learning, Tracking and Loop-Closing
(2401.09160)Abstract
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.
Overview
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SLAM technology is essential for autonomous navigation in vehicles, drones, and robots, but struggles with dynamic environments.
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DK-SLAM introduces adaptive deep local features through Meta-Learning, enhancing environmental adaptation without losing previous knowledge.
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The system uses a two-stage tracking approach with a direct method and feature matching refinement to improve positioning.
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Experiments reveal DK-SLAM's superior performance to other SLAM frameworks in various outdoor and indoor settings.
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Future research will tackle improving system efficiency and possibly incorporate knowledge distillation techniques for optimization.
Introduction to DK-SLAM
Simultaneous Localization and Mapping (SLAM) technology is critical to the backbone of autonomous systems, including cars, drones, and robots, enabling them to navigate and understand their environments. Recent advances have seen the rise of deep learning techniques that have improved the performance of visual SLAM systems. However, despite significant progress, challenges persist, particularly with handcrafted visual features that falter in dynamically lit or textured environments. This necessitates the development of SLAM systems that can adapt and learn robust features to operate effectively across a range of real-world scenes.
Innovation in DK-SLAM
A key innovation of the proposed DK-SLAM framework is the integration of adaptive deep local features into the SLAM process. The system employs a Meta-Learning strategy, specifically Model-Agnostic Meta-Learning (MAML), to enable fast and efficient training of deep feature extractors. This training schema significantly enhances the system's capacity to adapt to varied scenarios without compromising the learned knowledge. In a two-stage tracking approach, the framework initially employs a direct method to roughly estimate the pose between frames, followed by a feature matching refinement for exact pose estimation. By addressing the issue of cumulative positioning error, DK-SLAM presents an online learning binary feature-based module that ensures accurate detection of loops within sequences, a crucial factor for any SLAM system's efficacy.
Scalability and Performance
Experimentation showcases DK-SLAM's superior performance, particularly when compared against notable frameworks like ORB-SLAM3. The experiments involved publicly available datasets like KITTI and EuRoC, demonstrating DK-SLAM's edge in handling realistic and diverse traffic scenarios in outdoor settings, and the challenges of indoor navigation encountered by micro aerial vehicles (MAVs). Notably, DK-SLAM's online binary BoW consistently displayed an impressive recall rate in loop closure tests, underscoring the system’s robustness.
Future Directions
While DK-SLAM paves the way for more robust and adaptable visual SLAM systems, the interplay between GPU-based online front-end processes and CPU-based back-end tasks does surface efficiency issues. Moving forward, research will focus on improving system efficiency, potentially exploring knowledge distillation techniques for parameter reduction and comprehensive optimizations within the SLAM framework. With continuous enhancements, DK-SLAM is set to advance the frontier of intelligent autonomous navigation in both familiar and unstructured environments.
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