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.

翻译：单目稠密同步定位与建图（SLAM）的应用常受限于高延迟、巨大的GPU内存消耗以及对相机标定的依赖。为缓解这些限制，我们提出了EC3R-SLAM，一种新颖的免标定单目稠密SLAM框架，该框架同时实现了高定位与建图精度、低延迟以及低GPU内存消耗。该框架通过耦合一个跟踪模块（用于维护特征点的稀疏地图）和一个基于前馈三维重建模型（同时估计相机内参）的建图模块来实现高效性。此外，系统同时融入了局部与全局闭环检测，以确保中期与长期的数据关联，从而加强多视图一致性，进而提升系统的整体精度与鲁棒性。在多个基准测试上的实验表明，EC3R-SLAM与现有先进方法相比取得了具有竞争力的性能，同时速度更快、内存效率更高。此外，它即使在笔记本电脑和Jetson Orin NX等资源受限的平台上也能有效运行，凸显了其在现实世界机器人应用中的潜力。