  While interacting in the world is a multi-sensory experience, many robots
continue to predominantly rely on visual perception to map and navigate in
their environments. In this work, we propose Audio-Visual-Language Maps
(AVLMaps), a unified 3D spatial map representation for storing cross-modal
information from audio, visual, and language cues. AVLMaps integrate the
open-vocabulary capabilities of multimodal foundation models pre-trained on
Internet-scale data by fusing their features into a centralized 3D voxel grid.
In the context of navigation, we show that AVLMaps enable robot systems to
index goals in the map based on multimodal queries, e.g., textual descriptions,
images, or audio snippets of landmarks. In particular, the addition of audio
information enables robots to more reliably disambiguate goal locations.
Extensive experiments in simulation show that AVLMaps enable zero-shot
multimodal goal navigation from multimodal prompts and provide 50% better
recall in ambiguous scenarios. These capabilities extend to mobile robots in
the real world - navigating to landmarks referring to visual, audio, and
spatial concepts. Videos and code are available at: https://avlmaps.github.io.
