  Object recognition in unseen indoor environments remains a challenging
problem for visual perception of mobile robots. In this letter, we propose the
use of topologically persistent features, which rely on the objects' shape
information, to address this challenge. In particular, we extract two kinds of
features, namely, sparse persistence image (PI) and amplitude, by applying
persistent homology to multi-directional height function-based filtrations of
the cubical complexes representing the object segmentation maps. The features
are then used to train a fully connected network for recognition. For
performance evaluation, in addition to a widely used shape dataset and a
benchmark indoor scenes dataset, we collect a new dataset, comprising scene
images from two different environments, namely, a living room and a mock
warehouse. The scenes are captured using varying camera poses under different
illumination conditions and include up to five different objects from a given
set of fourteen objects. On the benchmark indoor scenes dataset, sparse PI
features show better recognition performance in unseen environments than the
features learned using the widely used ResNetV2-56 and EfficientNet-B4 models.
Further, they provide slightly higher recall and accuracy values than Faster
R-CNN, an end-to-end object detection method, and its state-of-the-art variant,
Domain Adaptive Faster R-CNN. The performance of our methods also remains
relatively unchanged from the training environment (living room) to the unseen
environment (mock warehouse) in the new dataset. In contrast, the performance
of the object detection methods drops substantially. We also implement the
proposed method on a real-world robot to demonstrate its usefulness.
