  Recognition of occluded objects in unseen indoor environments is a
challenging problem for mobile robots. This work proposes a new slicing-based
topological descriptor that captures the 3D shape of object point clouds to
address this challenge. It yields similarities between the descriptors of the
occluded and the corresponding unoccluded objects, enabling object unity-based
recognition using a library of trained models. The descriptor is obtained by
partitioning an object's point cloud into multiple 2D slices and constructing
filtrations (nested sequences of simplicial complexes) on the slices to mimic
further slicing of the slices, thereby capturing detailed shapes through
persistent homology-generated features. We use nine different sequences of
cluttered scenes from a benchmark dataset for performance evaluation. Our
method outperforms two state-of-the-art deep learning-based point cloud
classification methods, namely, DGCNN and SimpleView.
