物体認識なしで屋内環境の高レベル意味領域をマッピング
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition
視覚言語モデルと自律ナビゲーションを用いて、物体認識に頼らずに屋内環境の意味領域マップを生成する手法を提案し、フォトリアルシミュレータで既存手法を上回る性能を示した。
著者: Roberto Bigazzi, Lorenzo Baraldi, Shreyas Kousik, Rita Cucchiara, Marco Pavone
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments. In the literature, there has been an extensive focus on object labeling and exhaustive scene graph generation; less effort has been focused on the task of purely identifying and mapping large semantic regions. The present work proposes a method for semantic region mapping via embodied navigation in indoor environments, generating a high-level representation of the knowledge of the agent. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location. This mapping procedure is paired with a trained navigation policy to enable autonomous map generation. The proposed method significantly outperforms a variety of baselines, including an object-based system and a pretrained scene classifier, in experiments in a photorealistic simulator.
