解决单目SLAM尺度漂移问题，实现大场景下精准三维重建。
SCE-SLAM: Scale-Consistent Monocular SLAM via Scene Coordinate Embeddings
- 用场景坐标嵌入学习3D几何关系，保持尺度一致性。
- 在KITTI上轨迹误差减少8.36米，实时运行达36帧/秒。
- 适合资源受限平台的长期自主导航与重建任务。
单目视觉SLAM可从互联网视频中实现三维重建，并在资源受限平台上支持自主导航，但存在尺度漂移问题，即长序列中估计尺度逐渐发散。现有帧间方法通过局部优化实现实时性，但因独立窗口间缺乏全局约束而累积尺度漂移。为此，我们提出SCE-SLAM，一种端到端的SLAM系统，通过场景坐标嵌入维持尺度一致性。该嵌入为分块级表示，编码在标准尺度参考下的3D几何关系。系统包含两个核心模块：几何引导聚合利用3D空间邻近性，通过几何调制注意力将历史观测的尺度信息传播；场景坐标束调整通过解码场景坐标嵌入中的显式3D坐标约束，将当前估计锚定于参考尺度。在KITTI、Waymo和vKITTI上的实验表明显著提升：相比最优前序方法，我们的方法在KITTI上绝对轨迹误差减少8.36米，同时保持36 FPS，实现大场景下的尺度一致性。
原文摘要 · Abstract (English)
Monocular visual SLAM enables 3D reconstruction from internet video and autonomous navigation on resource-constrained platforms, yet suffers from scale drift, i.e., the gradual divergence of estimated scale over long sequences. Existing frame-to-frame methods achieve real-time performance through local optimization but accumulate scale drift due to the lack of global constraints among independent windows. To address this, we propose SCE-SLAM, an end-to-end SLAM system that maintains scale consistency through scene coordinate embeddings, which are learned patch-level representations encoding 3D geometric relationships under a canonical scale reference. The framework consists of two key modules: geometry-guided aggregation that leverages 3D spatial proximity to propagate scale information from historical observations through geometry-modulated attention, and scene coordinate bundle adjustment that anchors current estimates to the reference scale through explicit 3D coordinate constraints decoded from the scene coordinate embeddings. Experiments on KITTI, Waymo, and vKITTI demonstrate substantial improvements: our method reduces absolute trajectory error by 8.36m on KITTI compared to the best prior approach, while maintaining 36 FPS and achieving scale consistency across large-scale scenes.
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