  Cameras and 2D laser scanners, in combination, are able to provide low-cost,
light-weight and accurate solutions, which make their fusion well-suited for
many robot navigation tasks. However, correct data fusion depends on precise
calibration of the rigid body transform between the sensors. In this paper we
present the first framework that makes use of Convolutional Neural Networks
(CNNs) for odometry estimation fusing 2D laser scanners and mono-cameras. The
use of CNNs provides the tools to not only extract the features from the two
sensors, but also to fuse and match them without needing a calibration between
the sensors. We transform the odometry estimation into an ordinal
classification problem in order to find accurate rotation and translation
values between consecutive frames. Results on a real road dataset show that the
fusion network runs in real-time and is able to improve the odometry estimation
of a single sensor alone by learning how to fuse two different types of data
information.
