LagMemo: 言語3Dガウススプラッティング記憶によるマルチモーダル・オープンボキャブラリ・マルチゴール視覚ナビゲーション
LagMemo: Language 3D Gaussian Splatting Memory for Multi-modal Open-vocabulary Multi-goal Visual Navigation
一度の探索で言語埋め込み付き3Dガウススプラッティング記憶を構築し、テキストや画像などの多様な目標クエリに対して候補地点を検索・検証してロボットを誘導する視覚ナビゲーションシステムを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Haotian Zhou, Xiaole Wang, He Li, Zhuo Qi, Jinrun Yin, Haiyu Kong, Jianghuan Xu, Huijing Zhao
分類: cs.RO, cs.AI
原文アブストラクト
Navigating to a designated goal using visual information is a fundamental capability for intelligent robots. To address the practical demands of multi-modal, open-vocabulary goal queries and multi-goal visual navigation, we propose LagMemo, a navigation system that leverages a language 3D Gaussian Splatting memory. During a one-time exploration, LagMemo constructs a unified 3D language memory with robust spatial-semantic correlations. With incoming task goals, the system efficiently queries the memory, predicts candidate goal locations, and integrates a local perception-based verification mechanism to dynamically match and validate goals. For fair and rigorous evaluation, we curate GOAT-Core, a high-quality core split distilled from GOAT-Bench. Experimental results show that LagMemo's memory module enables effective multi-modal open-vocabulary localization, and significantly outperforms state-of-the-art methods in multi-goal visual navigation. Project page: https://weekgoodday.github.io/lagmemo
