无需标定的单目稠密建图，兼顾精度与实时性。
EC3R-SLAM: Efficient and Consistent Monocular Dense SLAM with Feed-Forward 3D Reconstruction
- 耦合追踪与前馈3D重建，同步估计相机参数
- 低延迟、低显存，支持中长时回环优化
- 可在笔记本和Jetson平台运行，适合机器人落地
单目稠密视觉定位与建图（SLAM）常受高延迟、大显存占用和依赖相机标定的限制。为此，我们提出EC3R-SLAM，一种无需标定的新型单目稠密SLAM框架，联合实现高定位与建图精度、低延迟和低显存消耗。该框架通过耦合跟踪模块（维护特征点稀疏地图）与基于前馈3D重建模型的映射模块，同步估计相机内参。同时引入局部与全局回环检测，保障中长期数据关联，提升多视角一致性，从而增强系统整体精度与鲁棒性。多个基准测试显示，EC3R-SLAM性能媲美顶尖方法，且更快速、更省显存。其在资源受限平台（如笔记本、Jetson Orin NX）上仍能有效运行，具备实际机器人应用潜力。
原文摘要 · Abstract (English)
The application of monocular dense Simultaneous Localization and Mapping (SLAM) is often hindered by high latency, large GPU memory consumption, and reliance on camera calibration. To relax this constraint, we propose EC3R-SLAM, a novel calibration-free monocular dense SLAM framework that jointly achieves high localization and mapping accuracy, low latency, and low GPU memory consumption. This enables the framework to achieve efficiency through the coupling of a tracking module, which maintains a sparse map of feature points, and a mapping module based on a feed-forward 3D reconstruction model that simultaneously estimates camera intrinsics. In addition, both local and global loop closures are incorporated to ensure mid-term and long-term data association, enforcing multi-view consistency and thereby enhancing the overall accuracy and robustness of the system. Experiments across multiple benchmarks show that EC3R-SLAM achieves competitive performance compared to state-of-the-art methods, while being faster and more memory-efficient. Moreover, it runs effectively even on resource-constrained platforms such as laptops and Jetson Orin NX, highlighting its potential for real-world robotics applications.
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