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.
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 widely used Faster R-CNN and its state-of-the-art variant, Domain Adaptive Faster R-CNN , drops significantly. Moreover , the use of sparse PI features yields slightly higher overall recall and accuracy in the unseen warehouse environment . We also implement the proposed method on a real-world robot to demonstrate its usefulness.
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widely-used
widely used
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widely used Faster R-CNN and its
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variant, Domain Adaptive
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method decreases significantly. In fact
, drops significantly. Moreover
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slightly
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; and, better F1 scores on many of the individual object classes
in the unseen warehouse environment