AIM-SLAM achieves robust real-time SLAM performance across diverse environments.
Recent advances in geometric foundation models have emerged as a promising alternative for addressing the challenge of dense reconstruction in monocular visual simultaneous localization and mapping (SLAM). Although geometric foundation models enable SLAM to leverage variable input views, the previous methods remain confined to two-view pairs or fixed-length inputs without sufficient deliberation of geometric context for view selection.
To tackle this problem, we propose AIM-SLAM, a dense monocular SLAM framework that exploits an adaptive and informative multi-view keyframe prioritization with dense pointmap predictions from visual geometry grounded transformer (VGGT). Specifically, we introduce the selective information- and geometric-aware multi-view adaptation (SIGMA) module, which employs voxel overlap and information gain to retrieve a candidate set of keyframes and adaptively determine its size. Furthermore, we formulate a joint multi-view Sim(3) optimization that enforces consistent alignment across selected views, substantially improving pose estimation accuracy. The effectiveness of AIM-SLAM is demonstrated on real-world datasets, where it achieves state-of-the-art pose estimation performance and accurate dense reconstruction results. Our system supports ROS integration.
Overall architecture of AIM-SLAM. The frontend consists of (a) multi-view prioritization method via the proposed SIGMA module, followed by VGGT-based dense pointmap inference, and (b) joint multi-view Sim(3) optimization to mitigate short- and mid-term drift. The backend loop closure module performs global pose-graph optimization to ensure global consistency.
EuRoC
TUM RGB-D
@inproceedings{jeon2026aim,
title = {{AIM-SLAM}: Dense Monocular {SLAM} via Adaptive and Informative Multi-View Keyframe Prioritization with Foundation Model},
author = {Jinwoo Jeon, Dong-Uk Seo, Eungchang Mason Lee, Hyun Myung},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {2026}
}