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Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. In recent years, there is a high exploitation of artificial intelligence algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. First, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Second, each layer of data are fed into a deep neural network for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained …