基于3D重建先验的实时单目稠密SLAM，无需固定相机模型
MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors
- 利用MASt3R的两视图3D重建先验，实现无需固定相机模型的稠密定位
- 在真实场景视频上实现15帧/秒的实时运行，全局一致性良好
- 适合需要高精度三维重建的移动机器人与AR应用
我们提出一种基于MASt3R（一个两视图3D重建与匹配先验）自底向上设计的实时单目稠密SLAM系统。得益于这一强先验，该系统在真实世界视频序列中表现鲁棒，且不假设固定或参数化相机模型，仅要求相机中心唯一。我们引入了高效的点云匹配、相机跟踪、局部融合、图构建与回环闭合方法，以及二阶全局优化。在已知标定条件下，仅需简单修改即可在多个基准测试中达到顶尖性能。整体上，我们提供了一个即插即用的单目SLAM系统，能在15 FPS下生成全局一致的姿态与稠密几何结构。
原文摘要 · Abstract (English)
We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this strong prior, our system is robust on in-the-wild video sequences despite making no assumption on a fixed or parametric camera model beyond a unique camera centre. We introduce efficient methods for pointmap matching, camera tracking and local fusion, graph construction and loop closure, and second-order global optimisation. With known calibration, a simple modification to the system achieves state-of-the-art performance across various benchmarks. Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally-consistent poses and dense geometry while operating at 15 FPS.
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