  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.
