  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.
