  Recognition of occluded objects in unseen and unstructured indoor
environments is a challenging problem for mobile robots. To address this
challenge, we propose a new descriptor, TOPS, for point clouds generated from
depth images and an accompanying recognition framework, THOR, inspired by human
reasoning. The descriptor employs a novel slicing-based approach to compute
topological features from filtrations of simplicial complexes using persistent
homology, and facilitates reasoning-based recognition using object unity. Apart
from a benchmark dataset, we report performance on a new dataset, the UW Indoor
Scenes (UW-IS) Occluded dataset, curated using commodity hardware to reflect
real-world scenarios with different environmental conditions and degrees of
object occlusion. THOR outperforms state-of-the-art methods on both the
datasets and achieves substantially higher recognition accuracy for all the
scenarios of the UW-IS Occluded dataset. Therefore, THOR, is a promising step
toward robust recognition in low-cost robots, meant for everyday use in indoor
settings.
