埋め込みポーズグラフ：コンパクト表現で3D基盤モデル機能を実現
Embedding Pose Graph, Enabling 3D Foundation Model Capabilities with a Compact Representation
ポーズグラフのノードに基盤モデルの特徴量を付与することで、ボクセルや点群より軽量かつスケーラブルに3D空間を表現し、開語彙クエリや言語誘導ナビゲーション、再位置推定などを可能にする手法を提案した。
著者: Hugues Thomas, Mouli Sivapurapu, Jian Zhang
分類: cs.RO
原文アブストラクト
This paper presents the Embedding Pose Graph (EPG), an innovative method that combines the strengths of foundation models with a simple 3D representation suitable for robotics applications. Addressing the need for efficient spatial understanding in robotics, EPG provides a compact yet powerful approach by attaching foundation model features to the nodes of a pose graph. Unlike traditional methods that rely on bulky data formats like voxel grids or point clouds, EPG is lightweight and scalable. It facilitates a range of robotic tasks, including open-vocabulary querying, disambiguation, image-based querying, language-directed navigation, and re-localization in 3D environments. We showcase the effectiveness of EPG in handling these tasks, demonstrating its capacity to improve how robots interact with and navigate through complex spaces. Through both qualitative and quantitative assessments, we illustrate EPG's strong performance and its ability to outperform existing methods in re-localization. Our work introduces a crucial step forward in enabling robots to efficiently understand and operate within large-scale 3D spaces.
