  Place classification is a fundamental ability that a robot should possess to
carry out effective human-robot interactions. It is a nontrivial classification
problem which has attracted many research. In recent years, there is a high
exploitation of Artificial Intelligent 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 the
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. Firstly, we construct multiple layers of
laser range data to represent the environment information in different levels
of granularity. Secondly, each layer of data is fed into a deep neural network
model for classification, where a graph regularization is imposed to the deep
architecture for keeping local consistency between adjacent samples. Finally,
the predicted labels obtained from all the layers are fused based on confidence
trees to maximize the overall confidence. Experimental results validate the
effective- ness of our end-to-end place classification framework in which both
the multi-layer structure and the graph regularization promote the
classification performance. Furthermore, results show that the features
automatically learned from the raw input range data can achieve competitive
results to the features constructed based on statistical and geometrical
information.
