  Visual object recognition in unseen and cluttered indoor environments is a
challenging problem for mobile robots. This study presents a 3D shape and
color-based descriptor, TOPS2, for point clouds generated from RGB-D images and
an accompanying recognition framework, THOR2. The TOPS2 descriptor embodies
object unity, a human cognition mechanism, by retaining the slicing-based
topological representation of 3D shape from the TOPS descriptor while capturing
object color information through slicing-based color embeddings computed using
a network of coarse color regions. These color regions, analogous to the
MacAdam ellipses identified in human color perception, are obtained using the
Mapper algorithm, a topological soft-clustering technique. THOR2, trained using
synthetic data, demonstrates markedly improved recognition accuracy compared to
THOR, its 3D shape-based predecessor, on two benchmark real-world datasets: the
OCID dataset capturing cluttered scenes from different viewpoints and the UW-IS
Occluded dataset reflecting different environmental conditions and degrees of
object occlusion recorded using commodity hardware. THOR2 also outperforms
baseline deep learning networks, and a widely-used ViT adapted for RGB-D inputs
on both the datasets. Therefore, THOR2 is a promising step toward achieving
robust recognition in low-cost robots.
