  LiDAR odometry (LO) describes the task of finding an alignment of subsequent
LiDAR point clouds. This alignment can be used to estimate the motion of the
platform where the LiDAR sensor is mounted on. Currently, on the well-known
KITTI Vision Benchmark Suite state-of-the-art algorithms are non-learning
approaches. We propose a network architecture that learns LO by directly
processing 3D point clouds. It is trained on the KITTI dataset in an end-to-end
manner without the necessity of pre-defining corresponding pairs of points. An
evaluation on the KITTI Vision Benchmark Suite shows similar performance to a
previously published work, DeepCLR [1], even though our model uses only around
3.56% of the number of network parameters thereof. Furthermore, a plane point
extraction is applied which leads to a marginal performance decrease while
simultaneously reducing the input size by up to 50%.
