Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation

• URL: http://arxiv.org/abs/2403.17846v2
• Date: Mon, 3 Jun 2024 17:12:25 GMT
• Title: Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation
• Authors: Abdelrhman Werby, Chenguang Huang, Martin Büchner, Abhinav Valada, Wolfram Burgard,
• Abstract summary: We present HOV-SG, a hierarchical open-vocabulary 3D scene graph mapping approach for language-grounded robot navigation. HOV-SG is evaluated on three distinct datasets and surpasses previous baselines in open-vocabulary semantic accuracy on the object, room, and floor level. In order to prove the efficacy and generalization capabilities of HOV-SG, we showcase successful language-conditioned robot navigation within real-world multi-storage environments.
• Score: 22.789590144545706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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