We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts. The recently proposed RNR-Map employs a grid structure comprising latent codes positioned at each pixel. These latent codes, which are derived from image observation, enable: i) image rendering given a camera pose, since they are converted to Neural Radiance Field; ii) image navigation and localization with astonishing accuracy. On top of this, we enhance RNR-Map with CLIP-based embedding latent codes, allowing natural language search without additional label data. We evaluate the effectiveness of this map in single and multi-object searches. We also investigate its compatibility with a Large Language Model as an "affordance query resolver". Code and videos are available at https://intelligolabs.github.io/Le-RNR-Map/
@article{arxiv.2308.08854,
title = {Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language},
author = {Francesco Taioli and Federico Cunico and Federico Girella and Riccardo Bologna and Alessandro Farinelli and Marco Cristani},
journal= {arXiv preprint arXiv:2308.08854},
year = {2023}
}