  In visual Simultaneous Localization And Mapping (SLAM), detecting loop
closures has been an important but difficult task. Currently, most solutions
are based on the bag-of-words approach. Yet the possibility of deep neural
network application to this task has not been fully explored due to the lack of
appropriate architecture design and of sufficient training data. In this paper
we demonstrate the applicability of deep neural networks by addressing both
issues. Specifically we show that a feature pyramid Siamese neural network can
achieve state-of-the-art performance on pairwise loop closure detection. The
network is trained and tested on large-scale RGB-D datasets with a novel
automatic loop closure labeling algorithm. Each image pair is labelled by how
much the images overlap, allowing loop closure to be computed directly rather
than by labor intensive manual labeling. We present an algorithm to adopt any
large-scale generic RGB-D dataset for use in training deep loop-closure
networks. We show for the first time that deep neural networks are capable of
detecting loop closures, and we provide a method for generating large-scale
datasets for use in evaluating and training loop closure detectors.
