SCIM: オープンワールド意味理解のための同時クラスタリング・推論・マッピング
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding
未知環境でロボットが新規物体を自律的に発見し、既知クラスの精度を向上させるため、マッピングとクラスタリングを統合し自己教師あり学習信号を生成するフレームワークを開発した。
著者: Hermann Blum, Marcus G. Müller, Abel Gawel, Roland Siegwart, Cesar Cadena
分類: cs.RO, cs.CV
原文アブストラクト
In order to operate in human environments, a robot's semantic perception has to overcome open-world challenges such as novel objects and domain gaps. Autonomous deployment to such environments therefore requires robots to update their knowledge and learn without supervision. We investigate how a robot can autonomously discover novel semantic classes and improve accuracy on known classes when exploring an unknown environment. To this end, we develop a general framework for mapping and clustering that we then use to generate a self-supervised learning signal to update a semantic segmentation model. In particular, we show how clustering parameters can be optimized during deployment and that fusion of multiple observation modalities improves novel object discovery compared to prior work. Models, data, and implementations can be found at https://github.com/hermannsblum/scim
