UAV Real-Time Scene Modeling Optimization Based on Dynamic NeRF and SLAM

To address the problem of balancing the efficiency and accuracy of 3D mapping for UAVs in dynamic environments, this paper designs a scene modeling optimization solution that integrates dynamic NeRF with SLAM. The system achieves accurate suppression of motion interference by building a dynamic region elimination mechanism that combines optical flow residuals and semantic masks. At the same time, it introduces a pose-neural field collaborative optimization strategy to enhance the spatial coherence and temporal stability of mapping. The lightweight structure of Instant-NGP is used to achieve real-time reasoning and adapt to low-power computing platforms. Empirical studies on Replica public datasets and real-life flight scenarios show that this method outperforms existing mainstream models in terms of PSNR, SSIM, trajectory accuracy, and dynamic robustness, reflecting significant engineering usability and deployment opportunities.