  The use of 2D laser scanners is attractive for the autonomous driving
industry because of its accuracy, light-weight and low-cost. However, since
only a 2D slice of the surrounding environment is detected at each scan, it is
a challenge to execute important tasks such as the localization of the vehicle.
In this paper we present a novel framework that explores the use of deep
Recurrent Convolutional Neural Networks (RCNN) for odometry estimation using
only 2D laser scanners. The application of RCNNs provides the tools to not only
extract the features of the laser scanner data using Convolutional Neural
Networks (CNNs), but in addition it models the possible connections among
consecutive scans using the Long Short-Term Memory (LSTM) Recurrent Neural
Network. Results on a real road dataset show that the method can run in
real-time without using GPU acceleration and have competitive performance
compared to other methods, being an interesting approach that could complement
traditional localization systems.
