マルチモーダルLLMとフィッシャー情報を用いた探索・アクティブマッピング
Multimodal LLM Guided Exploration and Active Mapping using Fisher Information
3Dガウシアンスプラッティング表現とマルチモーダルLLMを組み合わせ、長期的な探索目標と短期的な行動計画を同時に行うアクティブマッピングシステムを提案。位置推定の不確かさを考慮した情報利得最大化により、最先端の性能を実現した。
著者: Wen Jiang, Boshu Lei, Katrina Ashton, Kostas Daniilidis
分類: cs.RO, cs.CV
原文アブストラクト
We present an active mapping system that plans for both long-horizon exploration goals and short-term actions using a 3D Gaussian Splatting (3DGS) representation. Existing methods either do not take advantage of recent developments in multimodal Large Language Models (LLM) or do not consider challenges in localization uncertainty, which is critical in embodied agents. We propose employing multimodal LLMs for long-horizon planning in conjunction with detailed motion planning using our information-based objective. By leveraging high-quality view synthesis from our 3DGS representation, our method employs a multimodal LLM as a zero-shot planner for long-horizon exploration goals from the semantic perspective. We also introduce an uncertainty-aware path proposal and selection algorithm that balances the dual objectives of maximizing the information gain for the environment while minimizing the cost of localization errors. Experiments conducted on the Gibson and Habitat-Matterport 3D datasets demonstrate state-of-the-art results of the proposed method.
関連論文
- 探索しながら理解する：意味論駆動型アクティブマッピングアクティブマッピング
- 不確実性駆動型アクティブマッピングのためのニューラル可視性フィールドアクティブマッピング
