FVLoc-NeRF: Fast Vision-only Localization within Neural Radiation Field

In recent years, Neural Radiation Fields (NeRF) have shown tremendous potential in encoding highly-detailed 3D geometry and environmental appearance, thus making it a promising alternative to traditional explicit maps for robot localization. However, current NeRF localization methods suffer from significant computational overheads, primarily resulting from the large number of iterations or particle samples required, as well as the additional computational demands associated with the estimation of the initial pose through multimodal sensors. To overcome these challenges, we propose a novel and time-efficient NeRF localization pipeline, named FVLoc-NeRF. This pipeline solely employs RGB monocular images as input and leverages a retrieval method to obtain the initial pose. Subsequently, the pose update is derived using the Perspective-n-Point (PnP) algorithm, thereby considerably reducing the number of iterations and accelerating the localization process. Our extensive experimental results clearly demonstrate that FVLoc-NeRF is much faster than the state-of-the-art method.