Object recognition in unseen indoor environments has been challenging for most state-of-the-art object detectors. To address this challenge , we propose the use of topologically persistent features for object recognition. We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features,  by applying persistent homology to  filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions . The features are used for training a fully connected network for recognition. For performance evaluation, in addition to a widely used shape dataset, we collect new datasets comprising scene images from two different environments, i.e. , a living room and a warehouse. Scene images in both environments consist of up to five different objects with varying poses, chosen from a  set of fourteen objects , taken in varying illumination conditions from different distances and camera viewing angles. The overall performance of our methods, trained using living room images, remains relatively unaltered on the unseen warehouse images , without retraining . In contrast, a state-of-the-art object detector's accuracy drops considerably. Our methods also achieve higher overall recall and accuracy than the state-of-the-art in the unseen warehouse environment . We also implement the proposed framework on a real-world robot to demonstrate its usefulness.
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 shape information of the objects, 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, we collect a new dataset comprising scene images from two different environments, namely , a living room and a mock warehouse. The scenes in both the environments include up to five different objects that are chosen from a given set of fourteen objects . The objects have varying poses and arrangements, and are imaged under different illumination conditions and camera poses. The recognition performance of our methods, which are trained using the living room images, remains relatively unaffected on the unseen warehouse images . In contrast, the performance of the state-of-the-art Faster R-CNN method decreases significantly. In fact, the use of sparse PI features yields higher overall recall and accuracy ; and, better F1 scores on many of the individual object classes . We also implement the proposed method on a real-world robot to demonstrate its usefulness.
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