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We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map forVisual Navigation with natural language query prompts. The recently proposedRNR-Map employs a grid structure comprising latent codes positioned at eachpixel. These latent codes, which are derived from image observation, enable: i)image rendering given a camera pose, since they are converted to NeuralRadiance Field; ii) image navigation and localization with astonishingaccuracy. On top of this, we enhance RNR-Map with CLIP-based embedding latentcodes, allowing natural language search without additional label data. Weevaluate the effectiveness of this map in single and multi-object searches. Wealso investigate its compatibility with a Large Language Model as an"affordance query resolver". Code and videos are available athttps://intelligolabs.github.io/Le-RNR-Map/

Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language