为单目视觉-惯性系统使用前馈3D模型实现高效的特征-free初始化
Efficient Feature-Free Initialization for Monocular Visual-Inertial Systems Using a Feed-Forward 3D Model
AI总结：
本文提出了一种无需视觉特征跟踪的初始化框架，利用前馈3D模型预测的点云，从而提高了单目视觉-惯性导航系统的初始化可靠性与效率，实验表明其初始化成功率超过90%且数据需求显著减少。
摘要：
快速且可靠的初始化对于单目视觉-惯性导航系统（VINS）至关重要，因为它为后续的状态估计建立了初始条件。尽管已有显著进展，但大多数现有方法仍依赖于视觉特征对应关系，并需要3-4秒的传感器数据才能成功初始化，这限制了它们的应用性和效率。随着前馈3D模型的出现，这些模型可以直接从图像预测点云，我们重新从简洁的角度审视视觉-惯性初始化问题。在本文中，我们提出了一种特征-free初始化框架，利用前馈3D模型预测的点云，从而避免了视觉特征跟踪和估计的需要。这种设计显著降低了系统复杂性并提高了初始化的可靠性。在公开数据集上的实验表明，所提出的特征-free初始化方法实现了最高成功率，超过90%，并且显著减少了成功初始化所需的数据持续时间，通常降至1.2秒以下。我们进一步在自采集的数据集上验证了我们的方法，覆盖了各种室内和室外场景，展示了鲁棒性能，特别是在现有方法常失败的视觉退化环境中。代码和数据集可在https://github.com/Yuantai-Z/FF-VIO-Init获取。
英文摘要：
Fast and reliable initialization is critical for monocular visual-inertial navigation systems (VINS), as it establishes the starting conditions for subsequent state estimation. Despite steady progress, most existing methods heavily rely on visual feature correspondences and require 3-4 seconds of sensory data for successful initialization, which limits their applicability and efficiency. With the advent of feed-forward 3D models that can directly predict point clouds from images, we revisit the visual-inertial initialization problem from a concise perspective. In this work, we propose a feature-free initialization framework that leverages up-to-scale point clouds predicted by a feed-forward 3D model, thereby obviating the need for visual feature tracking and estimation. This design substantially reduces system complexity and improves the reliability of initialization. Experiments on public datasets demonstrate that the proposed feature-free initialization method achieves the highest success rate, exceeding 90%, and significantly reduces the data duration required for successful initialization, typically to under 1.2 s. We further validate our method on a self-collected dataset covering various indoor and outdoor scenarios, demonstrating robust performance, particularly in visually degraded environments where existing methods often fail. The code and dataset are available at https://github.com/Yuantai-Z/FF-VIO-Init.
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。
