Recent advancements in Simultaneous Localization and Mapping (SLAM) have increasingly highlighted the robustness of LiDAR-based techniques. At the same time, Neural Radiance Fields (NeRF) have introduced new possibilities for 3D scene reconstruction, exemplified by SLAM systems. Among these, NeRF-LOAM has shown notable performance in NeRF-based SLAM applications. However, despite its strengths, these systems often encounter difficulties in dynamic outdoor environments due to their inherent static assumptions. To address these limitations, this paper proposes a novel method designed to improve reconstruction in highly dynamic outdoor scenes. Based on NeRF-LOAM, the proposed approach consists of two primary components. First, we separate the scene into static background and dynamic foreground. By identifying and excluding dynamic elements from the mapping process, this segmentation enables the creation of a dense 3D map that accurately represents the static background only. The second component extends the octree structure to support multi-resolution representation. This extension not only enhances reconstruction quality but also aids in the removal of dynamic objects identified by the first module. Additionally, Fourier feature encoding is applied to the sampled points, capturing high-frequency information and leading to more complete reconstruction results. Evaluations on various datasets demonstrate that our method achieves more competitive results compared to current state-of-the-art approaches.

翻译：近年来，同步定位与建图（SLAM）领域的进展日益凸显了基于LiDAR技术的鲁棒性。与此同时，神经辐射场（NeRF）为三维场景重建带来了新的可能性，这在SLAM系统中得到了体现。其中，NeRF-LOAM在基于NeRF的SLAM应用中表现出了显著性能。然而，尽管具有这些优势，由于固有的静态场景假设，此类系统在动态室外环境中仍常面临困难。为应对这些局限，本文提出一种旨在改进高动态室外场景重建的新方法。该方法以NeRF-LOAM为基础，包含两个主要组成部分。首先，我们将场景分离为静态背景与动态前景。通过在建图过程中识别并排除动态元素，这种分割能够生成仅精确表征静态背景的稠密三维地图。第二部分将八叉树结构扩展为支持多分辨率表示。该扩展不仅提升了重建质量，亦有助于移除由第一模块识别的动态物体。此外，我们对采样点应用傅里叶特征编码，以捕获高频信息，从而获得更完整的重建结果。在多个数据集上的评估表明，与当前最先进方法相比，本方法取得了更具竞争力的成果。