This paper presents a new technique to solve the Indoor Visual Place Recognition problem from the Deep Learning perspective. It consists on an image retrieval approach supported by a novel image similarity metric. Our work uses a 3D laser sensor mounted on a backpack with a calibrated spherical camera i) to generate the data for training the deep neural network and ii) to build a database of geo-referenced images for an environment. The data collection stage is fully automatic and requires no user intervention for labelling. Thanks to the 3D laser measurements and the spherical panoramas, we can efficiently survey large indoor areas in a very short time. The underlying 3D data associated to the map allows us to define the similarity between two training images as the geometric overlap between the observed pixels. We exploit this similarity metric to effectively train a CNN that maps images into compact embeddings. The goal of the training is to ensure that the L2 distance between the embeddings associated to two images is small when they are observing the same place and large when they are observing different places. After the training, similarities between a query image and the geo-referenced images in the database are efficiently retrieved by performing a nearest neighbour search in the embeddings space.