Active Semantic Localization with Graph Neural Embedding. (arXiv:2305.06141v2 [cs.CV] UPDATED)

Semantic localization, i.e., robot self-localization with semantic image
modality, is critical in recently emerging embodied AI applications such as
point-goal navigation, object-goal navigation and vision language navigation.
However, most existing works on semantic localization focus on passive vision
tasks without viewpoint planning, or rely on additional rich modalities (e.g.,
depth measurements). Thus, the problem is largely unsolved. In this work, we
explore a lightweight, entirely CPU-based, domain-adaptive semantic
localization framework, called graph neural localizer.Our approach is inspired
by two recently …

ai applications applications arxiv embedding embodied embodied ai focus graph image language localization navigation planning robot semantic vision