  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.
