DK-SLAM：基于深度关键点学习、跟踪与回环检测的单目视觉SLAM
DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing
AI总结：
DK-SLAM提出一种结合MAML优化关键点网络、由粗到细跟踪及在线二进制特征回环检测的单目视觉SLAM，在公开数据集上超越ORB-SLAM3等系统，提升复杂环境下的定位精度与鲁棒性。
摘要：
在复杂真实场景中，视觉SLAM的性能常因使用手工特征而受到不可靠特征提取与匹配的影响。尽管基于深度学习的局部特征在捕捉高层信息方面表现出色，并在匹配基准上性能优异，但它们在连续运动场景中的泛化能力不足，对回环检测精度产生不利影响。我们的系统采用模型无关元学习（MAML）策略来优化关键点提取网络的训练，增强其对多样化环境的适应性。此外，我们引入了一种针对学习到的关键点的由粗到细的特征跟踪机制。该机制首先使用直接法近似估计连续帧之间的相对位姿，随后采用特征匹配方法进行精细化位姿估计。为缓解累积定位误差，DK-SLAM集成了一个新颖的在线学习模块，利用二进制特征进行回环检测。该模块在序列中动态识别回环节点，确保定位的准确性和高效性。在公开数据集上的实验评估表明，DK-SLAM优于领先的传统和基于学习的SLAM系统，如ORB-SLAM3和LIFT-SLAM。这些结果凸显了DK-SLAM在多样且具有挑战性的真实环境中的有效性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
