AIM-SLAM: Dense Monocular SLAM via Adaptive and Informative Multi-View Keyframe Prioritization with Foundation Model

We propose AIM-SLAM, a dense monocular SLAM framework that leverages dense pointmap predictions from a geometric foundation model (VGGT) and adaptively prioritizes informative multi-view keyframes. Our SIGMA module retrieves a candidate keyframe set via voxel overlap and information gain and adaptively determines its size, while a joint multi-view Sim(3) optimization enforces consistent alignment across the selected views, achieving state-of-the-art pose estimation and accurate dense reconstruction with ROS integration.