  Processing point clouds using deep neural networks is still a challenging
task. Most existing models focus on object detection and registration with deep
neural networks using point clouds. In this paper, we propose a deep model that
learns to estimate odometry in driving scenarios using point cloud data. The
proposed model consumes raw point clouds in order to extract frame-to-frame
odometry estimation through a hierarchical model architecture. Also, a local
bundle adjustment variation of this model using LSTM layers is implemented.
These two approaches are comprehensively evaluated and are compared against the
state-of-the-art.
