DIN-SLAM: Neural Radiance Field-Based SLAM with Depth Gradient and Sparse Optical Flow for Dynamic Interference Resistance

The neural implicit SLAM system performs excellently in static environments, offering higher-quality rendering and scene reconstruction capabilities compared to traditional dense SLAM. However, in dynamic real-world scenes, these systems often experience tracking drift and mapping errors. To address these problems, we suggest DIN-SLAM, a dynamic scene neural implicit SLAM system based on optical flow and depth gradient verification. DIN-SLAM combines depth gradients, optical flow, and motion consistency to achieve robust filtering of dynamic pixels, while optimizing dynamic feature points through optical flow registration to enhance tracking accuracy. The system also introduces a dynamic scene optimization strategy that utilizes photometric consistency loss, depth gradient loss, motion consistency constraints, and edge matching constraints to improve geometric consistency and reconstruction performance in dynamic environments. To reduce the interference of dynamic objects on scene reconstruction and eliminate artifacts in scene updates, we propose a targeted rendering and ray sampling strategy based on local feature counts, effectively mitigating the impact of dynamic object occlusions on reconstruction. Our method supports multiple sensor inputs, including pure RGB and RGB-D. The experimental results demonstrate that our approach consistently outperforms state-of-the-art baseline methods, achieving an 83.4% improvement in Absolute Trajectory Error Root Mean Square Error (ATE RMSE), a 91.7% enhancement in Peak Signal-to-Noise Ratio (PSNR), and the elimination of artifacts caused by dynamic interference. These enhancements significantly boost the performance of tracking and mapping in dynamic scenes.