言語に基づくロボットナビゲーションのための階層的オープン語彙3Dシーングラフ
Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation
オープン語彙の視覚基盤モデルを用いて、床・部屋・物体の階層を持つ3Dシーングラフを構築し、多階層建物内での言語指示に基づくロボットナビゲーションを実現した。
著者: Abdelrhman Werby, Chenguang Huang, Martin Büchner, Abhinav Valada, Wolfram Burgard
分類: cs.RO, cs.AI, cs.CL, cs.CV, cs.LG
原文アブストラクト
Recent open-vocabulary robot mapping methods enrich dense geometric maps with pre-trained visual-language features. While these maps allow for the prediction of point-wise saliency maps when queried for a certain language concept, large-scale environments and abstract queries beyond the object level still pose a considerable hurdle, ultimately limiting language-grounded robotic navigation. In this work, we present HOV-SG, a hierarchical open-vocabulary 3D scene graph mapping approach for language-grounded robot navigation. Leveraging open-vocabulary vision foundation models, we first obtain state-of-the-art open-vocabulary segment-level maps in 3D and subsequently construct a 3D scene graph hierarchy consisting of floor, room, and object concepts, each enriched with open-vocabulary features. Our approach is able to represent multi-story buildings and allows robotic traversal of those using a cross-floor Voronoi graph. HOV-SG is evaluated on three distinct datasets and surpasses previous baselines in open-vocabulary semantic accuracy on the object, room, and floor level while producing a 75% reduction in representation size compared to dense open-vocabulary maps. In order to prove the efficacy and generalization capabilities of HOV-SG, we showcase successful long-horizon language-conditioned robot navigation within real-world multi-storage environments. We provide code and trial video data at http://hovsg.github.io/.
