双视图回环检测：重访用于通过单目先验实现可扩展轨迹估计的PGO
Loop Closure from Two Views: Revisiting PGO for Scalable Trajectory Estimation through Monocular Priors
AI总结：
针对传统视觉SLAM难以兼顾效率与精度的问题，提出基于双视图回环、无需重建的2GO系统，借助图像匹配与单目深度先验实现无BA的高精度轨迹优化，可扩展且支持实时运行，拓展了大规模长期部署能力。
摘要：
（视觉）同时定位与建图（SLAM）仍是实现自主系统在大规模环境中导航并理解环境的一项核心挑战。传统SLAM方法难以在效率与精度之间取得平衡，在需要大量计算资源进行场景重建和光束法平差（BA）的大规模场景中尤为如此。然而，这种以视觉路标稀疏点云形式存在的场景重建通常仅在SLAM系统内部使用，因为导航与规划方法需要不同的地图表示形式。\n因此在本研究中，我们探究了一种更具可扩展性、无需重建的视觉SLAM（VSLAM）方法，该方法主要基于双视图回环检测技术。通过将地图限制为不含密集几何表示的稀疏关键帧位姿图，我们的2GO系统实现了高效优化，且绝对轨迹精度具备竞争力。尤其值得注意的是，我们发现图像匹配与单目深度先验领域的最新进展，使得无需BA即可实现极高精度的轨迹优化。\n我们在包含大规模场景的多样化数据集上开展了大量实验，并对运行时间、精度与地图规模之间的权衡关系进行了详细分析。结果表明，这种精简方法可支持实时性能，在地图规模和轨迹时长方面具备良好的可扩展性，有效拓展了VSLAM在大规模环境中长期部署的能力。
英文摘要：
(Visual) Simultaneous Localization and Mapping (SLAM) remains a fundamental challenge in enabling autonomous systems to navigate and understand large-scale environments. Traditional SLAM approaches struggle to balance efficiency and accuracy, particularly in large-scale settings where extensive computational resources are required for scene reconstruction and Bundle Adjustment (BA). However, this scene reconstruction, in the form of sparse pointclouds of visual landmarks, is often only used within the SLAM system because navigation and planning methods require different map representations. In this work, we therefore investigate a more scalable Visual SLAM (VSLAM) approach without reconstruction, mainly based on approaches for two-view loop closures. By restricting the map to a sparse keyframed pose graph without dense geometry representations, our `2GO' system achieves efficient optimization with competitive absolute trajectory accuracy. In particular, we find that recent advancements in image matching and monocular depth priors enable very accurate trajectory optimization without BA. We conduct extensive experiments on diverse datasets, including large-scale scenarios, and provide a detailed analysis of the trade-offs between runtime, accuracy, and map size. Our results demonstrate that this streamlined approach supports real-time performance, scales well in map size and trajectory duration, and effectively broadens the capabilities of VSLAM for long-duration deployments to large environments.
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