  This paper presents to the best of our knowledge the first end-to-end object
tracking approach which directly maps from raw sensor input to object tracks in
sensor space without requiring any feature engineering or system identification
in the form of plant or sensor models. Specifically, our system accepts a
stream of raw sensor data at one end and, in real-time, produces an estimate of
the entire environment state at the output including even occluded objects. We
achieve this by framing the problem as a deep learning task and exploit
sequence models in the form of recurrent neural networks to learn a mapping
from sensor measurements to object tracks. In particular, we propose a learning
method based on a form of input dropout which allows learning in an
unsupervised manner, only based on raw, occluded sensor data without access to
ground-truth annotations. We demonstrate our approach using a synthetic dataset
designed to mimic the task of tracking objects in 2D laser data -- as commonly
encountered in robotics applications -- and show that it learns to track many
dynamic objects despite occlusions and the presence of sensor noise.
