In this work we present a novel end-to-end framework for tracking and
classifying a robot's surroundings in complex, dynamic and only partially
observable real-world environments. The approach deploys a recurrent neural
network to filter an input stream of raw laser measurements in order to
directly infer object locations, along with their identity in both visible and
occluded areas. To achieve this we first train the network using unsupervised
Deep Tracking, a recently proposed theoretical framework for end-to-end space
occupancy prediction. We show that by learning to track on a large amount of
unsupervised data, the network creates a rich internal representation of its
environment which we in turn exploit through the principle of inductive
transfer of knowledge to perform the task of it's semantic classification. As a
result, we show that only a small amount of labelled data suffices to steer the
network towards mastering this additional task. Furthermore we propose a novel
recurrent neural network architecture specifically tailored to tracking and
semantic classification in real-world robotics applications. We demonstrate the
tracking and classification performance of the method on real-world data
collected at a busy road junction. Our evaluation shows that the proposed
end-to-end framework compares favourably to a state-of-the-art, model-free
tracking solution and that it outperforms a conventional one-shot training
scheme for semantic classification