论文标题
纬度：机器人全球本地化，在城市尺度NERF中具有截短的动态低通滤波器
LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF
论文作者
论文摘要
神经辐射场（NERF）在代表具有高分辨率细节和有效记忆的复杂3D场景方面取得了巨大成功。然而，当前基于NERF的姿势估计器没有初始姿势预测，并且在优化过程中易于局部优势。在本文中，我们介绍了纬度：带有截短的动态低通滤波器的全球定位，该过滤器引入了城市规模的NERF中的两阶段定位机制。在识别阶段，我们通过训练有素的NERFS生成的图像来训练回归器，该图像为全球本地化提供了初始值。在姿势优化阶段，我们通过直接优化切线平面上的姿势来最大程度地减少观察到的图像之间的残差和渲染图像。为了避免收敛到局部最佳，我们引入了截短的动态低通滤波器（TDLF），以进行粗到细节的姿势登记。我们在合成和现实世界中评估了我们的方法，并显示了其在大规模城市场景中高精度导航的潜在应用。代码和数据将在https://github.com/jike5/latitude上公开获取。
Neural Radiance Fields (NeRFs) have made great success in representing complex 3D scenes with high-resolution details and efficient memory. Nevertheless, current NeRF-based pose estimators have no initial pose prediction and are prone to local optima during optimization. In this paper, we present LATITUDE: Global Localization with Truncated Dynamic Low-pass Filter, which introduces a two-stage localization mechanism in city-scale NeRF. In place recognition stage, we train a regressor through images generated from trained NeRFs, which provides an initial value for global localization. In pose optimization stage, we minimize the residual between the observed image and rendered image by directly optimizing the pose on tangent plane. To avoid convergence to local optimum, we introduce a Truncated Dynamic Low-pass Filter (TDLF) for coarse-to-fine pose registration. We evaluate our method on both synthetic and real-world data and show its potential applications for high-precision navigation in large-scale city scenes. Codes and data will be publicly available at https://github.com/jike5/LATITUDE.
