未知環境での視覚的物体認識のための人間に着想を得たトポロジー表現
Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments
人間の「物体統一性」推論に着想を得て、色のトポロジカルなソフトクラスタリングと形状記述子を組み合わせたTOPS2記述子と認識フレームワークTHOR2を提案し、未知の混雑屋内環境での物体認識精度を向上させた。
著者: Ekta U. Samani, Ashis G. Banerjee
分類: cs.CV, cs.RO
原文アブストラクト
Visual object recognition in unseen and cluttered indoor environments is a challenging problem for mobile robots. Toward this goal, we extend our previous work to propose the TOPS2 descriptor, and an accompanying recognition framework, THOR2, inspired by a human reasoning mechanism known as object unity. We interleave color embeddings obtained using the Mapper algorithm for topological soft clustering with the shape-based TOPS descriptor to obtain the TOPS2 descriptor. THOR2, trained using synthetic data, achieves substantially higher recognition accuracy than the shape-based THOR framework and outperforms RGB-D ViT on two real-world datasets: the benchmark OCID dataset and the UW-IS Occluded dataset. Therefore, THOR2 is a promising step toward achieving robust recognition in low-cost robots.
