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Visual object recognition in unseen and cluttered indoor environments is achallenging problem for mobile robots. Toward this goal, we extend our previouswork to propose the TOPS2 descriptor, and an accompanying recognitionframework, THOR2, inspired by a human reasoning mechanism known as objectunity. We interleave color embeddings obtained using the Mapper algorithm fortopological soft clustering with the shape-based TOPS descriptor to obtain theTOPS2 descriptor. THOR2, trained using synthetic data, achieves substantiallyhigher recognition accuracy than the shape-based THOR framework and outperformsRGB-D ViT on two real-world datasets: the benchmark OCID dataset and the UW-ISOccluded dataset. Therefore, THOR2 is a promising step toward achieving robustrecognition in low-cost robots.

Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments