# RoDyn-SLAM: Robust Dynamic Dense RGB-D SLAM with Neural Radiance Fields

arXiv: https://arxiv.org/abs/2407.01303 · HTML: https://mlsanity.com/paper/2407.01303/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2024.

## Built on by (most cited first, 22 of 22)

- [WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments](https://mlsanity.com/paper/2504.03886/) (2025)
- [PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments](https://mlsanity.com/paper/2411.15800/) (2024)
- [VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes](https://mlsanity.com/paper/2501.08286/) (2025)
- [4D Gaussian Splatting SLAM](https://mlsanity.com/paper/2503.16710/) (2025)
- [DROID-SLAM in the Wild](https://mlsanity.com/paper/2603.19076/) (2026)
- [UP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments](https://mlsanity.com/paper/2505.22335/) (2025)
- [Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments](https://mlsanity.com/paper/2506.05965/) (2025)
- [Dream-SLAM: Dreaming the Unseen for Active SLAM in Dynamic Environments](https://mlsanity.com/paper/2602.21967/) (2026)
- [Flow4DGS-SLAM: Optical Flow-Guided 4D Gaussian Splatting SLAM](https://mlsanity.com/paper/2604.22339/) (2026)
- [Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM](https://mlsanity.com/paper/2504.04844/) (2025)
- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [CAD-SLAM: Consistency-Aware Dynamic SLAM with Dynamic-Static Decoupled Mapping](https://mlsanity.com/paper/2505.19420/) (2025)
- [ProDyG: Progressive Dynamic Scene Reconstruction via Gaussian Splatting from Monocular Videos](https://mlsanity.com/paper/2509.17864/) (2025)
- [KM-ViPE: Online Tightly Coupled Vision-Language-Geometry Fusion for Open-Vocabulary Semantic SLAM](https://mlsanity.com/paper/2512.01889/) (2025)
- [RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction](https://mlsanity.com/paper/2602.20807/) (2026)
- [RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments](https://mlsanity.com/paper/2604.26067/) (2026)
- [WildPose: A Unified Framework for Robust Pose Estimation in the Wild](https://mlsanity.com/paper/2605.12774/) (2026)
- [GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal](https://mlsanity.com/paper/2506.02736/) (2025)
- [Dropping the D: RGB-D SLAM Without the Depth Sensor](https://mlsanity.com/paper/2510.06216/) (2025)
- [ACE-SLAM: Scene Coordinate Regression for Neural Implicit Real-Time SLAM](https://mlsanity.com/paper/2512.14032/) (2025)
- [RoSe-SLAM: Robust Semantic-Aware Gaussian Splatting SLAM from Dynamic Monocular Videos](https://mlsanity.com/paper/2608.29003/) (2026)
- [SCOUT-SLAM: Structurally-Coupled Dual Uncertainty-Aware 3DGS SLAM in the Wild](https://mlsanity.com/paper/2609.14634/) (2026)

## Abstract (arXiv)

Leveraging neural implicit representation to conduct dense RGB-D SLAM has been studied in recent years. However, this approach relies on a static environment assumption and does not work robustly within a dynamic environment due to the inconsistent observation of geometry and photometry. To address the challenges presented in dynamic environments, we propose a novel dynamic SLAM framework with neural radiance field. Specifically, we introduce a motion mask generation method to filter out the invalid sampled rays. This design effectively fuses the optical flow mask and semantic mask to enhance the precision of motion mask. To further improve the accuracy of pose estimation, we have designed a divide-and-conquer pose optimization algorithm that distinguishes between keyframes and non-keyframes. The proposed edge warp loss can effectively enhance the geometry constraints between adjacent frames. Extensive experiments are conducted on the two challenging datasets, and the results show that RoDyn-SLAM achieves state-of-the-art performance among recent neural RGB-D methods in both accuracy and robustness.