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

Understand

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.