In this paper we propose a deep neural network based algorithm for indoor place recognition. It uses transfer learning to retrain VGG-F, a pretrained convolutional neural network to classify places on images acquired by a humanoid robot. The network has been trained as well as evaluated on a dataset consisting of 8000 images, which were recorded in sixteen rooms. The dataset is freely accessed from our website. We demonstrated experimentally that the proposed algorithm considerably outperforms BoW algorithms, which are frequently used in loop-closure. It also outperforms an algorithm in which features extracted by FC-6 layer of the VGG-F are classified by a linear SVM.
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