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Neural Radiance Fields (NeRFs) have made great success in representingcomplex 3D scenes with high-resolution details and efficient memory.Nevertheless, current NeRF-based pose estimators have no initial poseprediction and are prone to local optima during optimization. In this paper, wepresent LATITUDE: Global Localization with Truncated Dynamic Low-pass Filter,which introduces a two-stage localization mechanism in city-scale NeRF. Inplace recognition stage, we train a regressor through images generated fromtrained NeRFs, which provides an initial value for global localization. In poseoptimization stage, we minimize the residual between the observed image andrendered image by directly optimizing the pose on tangent plane. To avoidconvergence to local optimum, we introduce a Truncated Dynamic Low-pass Filter(TDLF) for coarse-to-fine pose registration. We evaluate our method on bothsynthetic and real-world data and show its potential applications forhigh-precision navigation in large-scale city scenes. Codes and data will bepublicly available at https://github.com/jike5/LATITUDE.

LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF