眼鏡はどこに置いた？実世界の半静的環境におけるオープン語彙セマンティック探索
Where Did I Leave My Glasses? Open-Vocabulary Semantic Exploration in Real-World Semi-Static Environments
家庭などの半静的環境で、物体の定位置らしさを確率的にモデル化して地図を維持し、LLM推論でオープン語彙の物体探索を効率化するシステムを提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Benjamin Bogenberger, Oliver Harrison, Orrin Dahanaggamaarachchi, Lukas Brunke, Jingxing Qian, Siqi Zhou, Angela P. Schoellig
分類: cs.RO
原文アブストラクト
Robots deployed in real-world environments, such as homes, must not only navigate safely but also understand their surroundings and adapt to changes in the environment. To perform tasks efficiently, they must build and maintain a semantic map that accurately reflects the current state of the environment. Existing research on semantic exploration largely focuses on static scenes without persistent object-level instance tracking. In this work, we propose an open-vocabulary, semantic exploration system for semi-static environments. Our system maintains a consistent map by building a probabilistic model of object instance stationarity, systematically tracking semi-static changes, and actively exploring areas that have not been visited for an extended period. In addition to active map maintenance, our approach leverages the map's semantic richness with large language model (LLM)-based reasoning for open-vocabulary object-goal navigation. This enables the robot to search more efficiently by prioritizing contextually relevant areas. We compare our approach against state-of-the-art baselines using publicly available object navigation and mapping datasets, and we further demonstrate real-world transferability in three real-world environments. Our approach outperforms the compared baselines in both success rate and search efficiency for object-navigation tasks and can more reliably handle changes in mapping semi-static environments. In real-world experiments, our system detects 95% of map changes on average, improving efficiency by more than 29% as compared to random and patrol strategies.
