基于关键帧的前馈视觉里程计
Keyframe-Based Feed-Forward Visual Odometry
AI总结：
本文提出基于关键帧的前馈视觉里程计方法，通过强化学习自适应选择关键帧，提升视觉里程计的性能。
摘要：
视觉基础模型的出现已经革新了视觉里程计（VO）和SLAM，使得在单个前馈网络中实现姿态估计和密集重建成为可能。然而，与传统管道利用关键帧方法来提高效率和精度不同，当前基于基础模型的方法，如VGGT-Long，通常对原始图像序列进行无差别处理。这导致了由于帧间视差低而造成的计算冗余和性能下降，因为低视差提供了有限的立体信息。将传统几何启发式方法整合到这些方法中是不平凡的，因为其性能依赖于高维潜在表示，而不是显式的几何度量。为了弥合这一差距，我们提出了一个新颖的关键帧前馈VO方法。不同于依赖手工规则，我们的方法采用强化学习来在数据驱动的方式下推导出一个自适应的关键帧策略，使选择与底层基础模型的内在特性对齐。我们训练我们的代理在TartanAir数据集上，并在多个现实世界数据集上进行了广泛的评估。实验结果表明，所提出的方法在状态-of-the-art前馈VO方法上实现了持续且显著的改进。
英文摘要：
The emergence of visual foundation models has revolutionized visual odometry~(VO) and SLAM, enabling pose estimation and dense reconstruction within a single feed-forward network. However, unlike traditional pipelines that leverage keyframe methods to enhance efficiency and accuracy, current foundation model based methods, such as VGGT-Long, typically process raw image sequences indiscriminately. This leads to computational redundancy and degraded performance caused by low inter-frame parallax, which provides limited contextual stereo information. Integrating traditional geometric heuristics into these methods is non-trivial, as their performance depends on high-dimensional latent representations rather than explicit geometric metrics. To bridge this gap, we propose a novel keyframe-based feed-forward VO. Instead of relying on hand-crafted rules, our approach employs reinforcement learning to derive an adaptive keyframe policy in a data-driven manner, aligning selection with the intrinsic characteristics of the underlying foundation model. We train our agent on TartanAir dataset and conduct extensive evaluations across several real-world datasets. Experimental results demonstrate that the proposed method achieves consistent and substantial improvements over state-of-the-art feed-forward VO methods.
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。
