以时空场景建模重新审视单目SLAM
Revisiting Monocular SLAM with Spatio-Temporal Scene Modeling
把前馈几何估计器（如单目深度/点图网络）当作 SLAM 的初始化与建图模块，用关键帧缓冲维持短期时序连续性、用 3D 体素场景模型做空间检索，这套「稀疏关键点定位 + 半稠密锚点空间表示」的混合设计可直接迁移到未标定单目实时定位与建图系统。
原文依据 a temporal representation based on a buffer of recent keyframes that preserves short-term scene continuity
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉SLAM是计算机视觉中最基础的问题之一，可直接应用于AR/VR、机器人和三维场景重建等实时定位任务。尽管稀疏方法与稠密方法均已取得显著进展，实时单目SLAM仍然充满挑战——尤其是在未标定设定下，现有方法往往效率低下且缺乏模块化。本文提出一条新的视觉SLAM流水线SLAM-MER，它用C++从零实现，显式利用SLAM问题的时空结构以提升定位效果，并采用模块化设计以便于集成现成组件。我们引入一种基于近期关键帧缓冲的时序表示，用以保持短期场景连续性；并辅以基于三维体素场景模型的空间表示，从而能够从先前重建的区域中高效检索相关三维点。借助近期的前馈几何估计器，我们的混合设计将基于稀疏关键点的定位与半稠密、锚点驱动的空间表示相结合。这一整合使我们达到实时性能（超过80 FPS），相比现有未标定单目SLAM流水线实现显著的效率提升，同时保持或改善定位精度。
英文原文摘要 1417 字符
Visual SLAM is one of the most fundamental problems in computer vision, with direct applications to real-time localization tasks such as AR/VR, robotics, and 3D scene reconstruction. Although significant progress has been made in both sparse and dense approaches, real-time monocular SLAM remains challenging--particularly in the uncalibrated setting, where existing methods are often inefficient and lack modularity. In this paper, we present a new visual SLAM pipeline, called SLAM-MER, which is implemented from scratch in C++ explicitly leveraging the spatio-temporal structure of the SLAM problem for improved localization, and has modular design to easily integrate off-the-shelf components. We introduce a temporal representation based on a buffer of recent keyframes that preserves short-term scene continuity. We complement this by incorporating a spatial representation based on a 3D cell-based scene model, enabling efficient retrieval of relevant 3D points from previously reconstructed regions. Leveraging recent feed-forward geometry estimators, our hybrid design combines sparse keypoint-based localization with a semi-dense anchor-point-driven spatial representation. This integration allows us to achieve real-time performance (exceeding 80 FPS) and a substantial efficiency improvement compared to existing uncalibrated monocular SLAM pipelines, while maintaining or improving localization accuracy.