针对动态户外场景，提升激光雷达建图与定位精度。
Neural Implicit Representation for Highly Dynamic LiDAR Mapping and Odometry
- 分离静态背景与动态前景，剔除动态物体干扰。
- 多分辨率八叉树结构提升重建质量，减少动态物体残留。
- 傅里叶特征编码捕捉高频细节，实现更完整重建。
近年来，基于激光雷达的同步定位与建图（SLAM）技术日益展现出其鲁棒性。与此同时，神经辐射场（NeRF）为三维场景重建带来了新可能，如NeRF-LOAM系统已在基于NeRF的SLAM中表现突出。然而，由于其固有的静态假设，这些系统在动态户外环境中常面临挑战。为此，本文提出一种新方法，以改进高度动态户外场景中的重建效果。基于NeRF-LOAM，该方法包含两个核心组件：首先，将场景分为静态背景与动态前景，通过识别并排除动态元素，实现仅对静态背景的稠密三维建图；其次，扩展八叉树结构以支持多分辨率表示，不仅提升重建质量，还辅助第一模块去除已识别的动态物体。此外，对采样点应用傅里叶特征编码，捕捉高频信息，带来更完整的重建结果。在多个数据集上的评估表明，该方法相较当前最先进方法更具竞争力。
原文摘要 · Abstract (English)
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.
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