AIM-SLAM：基于自适应和信息丰富的多视图关键帧优先级的密集单目SLAM
AIM-SLAM: Dense Monocular SLAM via Adaptive and Informative Multi-View Keyframe Prioritization with Foundation Model
AI总结：
AIM-SLAM通过自适应多视图关键帧优先级提升单目SLAM的密集重建性能，结合视觉几何基础模型实现更准确的位姿估计和一致的多视图对齐。
摘要：
近期几何基础模型的进步为解决单目视觉同时定位与建图（SLAM）中密集重建的挑战提供了一种有前景的替代方案。尽管几何基础模型使SLAM能够利用可变输入视角，但先前的方法仍局限于两视角对或固定长度输入，缺乏对视角选择的几何上下文的充分考虑。为了解决这个问题，我们提出了AIM-SLAM，一种利用视觉几何基础变换器（VGGT）进行密集点云预测的密集单目SLAM框架。具体来说，我们引入了选择性信息和几何感知的多视角适应（SIGMA）模块，该模块利用体素重叠和信息增益来检索候选关键帧集，并自适应确定其大小。此外，我们制定了联合多视角Sim(3)优化，强制在所选视角之间保持一致的对齐，显著提高了姿态估计的准确性。AIM-SLAM的有效性在现实世界数据集上得到了验证，其中它在姿态估计性能和准确的密集重建结果上实现了最先进的表现。我们的系统支持ROS集成，代码可在https://aimslam.github.io/上获得。
英文摘要：
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, with code is available at https://aimslam.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
