EC-SLAM: Effectively constrained neural RGB-D SLAM with TSDF hash encoding and joint optimization

We introduce EC-SLAM, a real-time dense RGB-D Simultaneous Localization and Mapping (SLAM) system leveraging Neural Radiance Fields (NeRF). While recent NeRF-based SLAM systems have shown promising results, they have yet to exploit NeRF’s potential to estimate system state fully. EC-SLAM overcomes this limitation by using a Truncated Signed Distance Fields (TSDF) opacity function with sharp inductive bias to strengthen constraints in sparse parametric encodings, which reduces the number of model parameters and enhances accuracy. Additionally, our system employs a highly constrained global joint optimization approach coupled with a feature-based, uniform sampling algorithm, enabling efficient fusion between TSDF and sparse parametric encodings. This approach reinforces constraints on keyframes most relevant to the current frame, mitigates the influence of random sampling, and effectively utilizes NeRF’s implicit loop closure capability. Extensive evaluations and ablations on the Replica, ScanNet, and TUM datasets demonstrate state-of-the-art performance, achieving precise tracking and reconstruction while maintaining real-time operation at up to 21 FPS. The source code is available at https://github.com/Lightingooo/EC-SLAM.