We propose Graph2Nav, a real-time 3D object-relation graph generation
framework, for autonomous navigation in the real world. Our framework fully
generates and exploits both 3D objects and a rich set of semantic relationships
among objects in a 3D layered scene graph, which is applicable to both indoor
and outdoor scenes. It learns to generate 3D semantic relations among objects,
by leveraging and advancing state-of-the-art 2D panoptic scene graph works into
the 3D world via 3D semantic mapping techniques. This approach avoids previous
training data constraints in learning 3D scene graphs directly from 3D data. Nous
conduct experiments to validate the accuracy in locating 3D objects and
labeling object-relations in our 3D scene graphs. We also evaluate the impact
of Graph2Nav via integration with SayNav, a state-of-the-art planner based on
large language models, on an unmanned ground robot to object search tasks in
real environments. Our results demonstrate that modeling object relations in
our scene graphs improves search efficiency in these navigation tasks.

Cet article explore les excursions dans le temps et leurs implications.

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