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.

翻译：本文提出一种实时单目稠密SLAM系统，其自底向上地构建于MASt3R——一种双视图三维重建与匹配先验模型之上。得益于这一强先验，我们的系统仅需假设唯一的相机中心，无需固定或参数化相机模型，即可在真实场景视频序列中保持鲁棒性。我们提出了高效的点云地图匹配、相机跟踪与局部融合、图构建与闭环检测以及二阶全局优化方法。在已知相机标定的情况下，通过对系统进行简单修改即可在多个基准测试中达到最先进的性能。整体而言，我们提出了一种即插即用的单目SLAM系统，能够以15帧/秒的速率生成全局一致的位姿与稠密几何重建结果。