見えないシーンを見る：部屋ナビゲーションのためのアモーダル意味地図学習
Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation
家の建築パターンを学習し、視野外の部屋の位置・大きさ・形を推定する意味地図を生成して、目標部屋への点ナビゲーション方策を訓練する手法を提案した。
著者: Medhini Narasimhan, Erik Wijmans, Xinlei Chen, Trevor Darrell, Dhruv Batra, Devi Parikh, Amanpreet Singh
分類: cs.CV, cs.RO
原文アブストラクト
We introduce a learning-based approach for room navigation using semantic maps. Our proposed architecture learns to predict top-down belief maps of regions that lie beyond the agent's field of view while modeling architectural and stylistic regularities in houses. First, we train a model to generate amodal semantic top-down maps indicating beliefs of location, size, and shape of rooms by learning the underlying architectural patterns in houses. Next, we use these maps to predict a point that lies in the target room and train a policy to navigate to the point. We empirically demonstrate that by predicting semantic maps, the model learns common correlations found in houses and generalizes to novel environments. We also demonstrate that reducing the task of room navigation to point navigation improves the performance further.
