EC3R-SLAM: Efficient and Consistent Monocular Dense SLAM with Feed-Forward 3D Reconstruction
Lingxiang Hu, Naima Ait Oufroukh, Fabien Bonardi, Raymond Ghandour
单目稠密同时定位与建图(SLAM)的应用常常受到高延迟、大GPU内存消耗以及对相机标定的依赖所限制。为了缓解这些约束，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、低延迟和低GPU内存消耗。这使得该框架能够通过跟踪模块（维护特征点的稀疏地图）和基于前馈三维重建模型（同时估计相机内参）的建图模块的耦合来实现高效性。此外，系统还融入了局部和全局闭环检测，以确保中期和长期的数据关联，强制多视图一致性，从而提升系统的整体精度和鲁棒性。在多个基准测试上的实验表明，EC3R-SLAM相比最先进方法实现了具有竞争力的性能，同时速度更快且内存效率更高。此外，它即使在资源受限的平台（如笔记本电脑和Jetson Orin NX）上也能有效运行，突显了其在现实世界机器人应用中的潜力。
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 ...