We introduce EC-SLAM, a real-time dense RGB-D simultaneous localization and mapping (SLAM) system utilizing Neural Radiance Fields (NeRF). Although recent NeRF-based SLAM systems have demonstrated encouraging outcomes, they have yet to completely leverage NeRF's capability to constrain pose optimization. By employing an effectively constrained global bundle adjustment (BA) strategy, our system makes use of NeRF's implicit loop closure correction capability. This improves the tracking accuracy by reinforcing the constraints on the keyframes that are most pertinent to the optimized current frame. In addition, by implementing a feature-based and uniform sampling strategy that minimizes the number of ineffective constraint points for pose optimization, we mitigate the effects of random sampling in NeRF. EC-SLAM utilizes sparse parametric encodings and the truncated signed distance field (TSDF) to represent the map in order to facilitate efficient fusion, resulting in reduced model parameters and accelerated convergence velocity. A comprehensive evaluation conducted on the Replica, ScanNet, and TUM datasets showcases cutting-edge performance, including enhanced reconstruction accuracy resulting from precise pose estimation, 21 Hz run time, and tracking precision improvements of up to 50\%. The source code is available at https://github.com/Lightingooo/EC-SLAM.

翻译：我们提出EC-SLAM，一种利用神经辐射场（NeRF）的实时密集RGB-D同时定位与地图构建（SLAM）系统。尽管近期基于NeRF的SLAM系统已展现出令人鼓舞的结果，但其尚未完全利用NeRF在约束位姿优化方面的能力。通过采用有效约束的全局集束调整（BA）策略，我们的系统利用了NeRF的隐式闭环校正能力。这一机制通过强化与待优化当前帧最相关的关键帧约束，提升了跟踪精度。此外，我们实施了一种基于特征且均匀的采样策略，最大限度地减少了位姿优化中无效约束点的数量，从而缓解了NeRF中随机采样的影响。EC-SLAM采用稀疏参数编码与截断符号距离场（TSDF）进行地图表示以促进高效融合，从而降低模型参数并加速收敛速度。在Replica、ScanNet和TUM数据集上的全面评估展示了最先进的性能，包括由精确位姿估计带来的重建精度提升、21 Hz的运行频率以及高达50%的跟踪精度改进。源代码已发布在https://github.com/Lightingooo/EC-SLAM。