  With the advent of powerful, light-weight 3D LiDARs, they have become the
hearth of many navigation and SLAM algorithms on various autonomous systems.
Pointcloud registration methods working with unstructured pointclouds such as
ICP are often computationally expensive or require a good initial guess.
Furthermore, 3D feature-based registration methods have never quite reached the
robustness of 2D methods in visual SLAM. With the continuously increasing
resolution of LiDAR range images, these 2D methods not only become applicable
but should exploit the illumination-independent modalities that come with it,
such as depth and intensity. In visual SLAM, deep learned 2D features and
descriptors perform exceptionally well compared to traditional methods. In this
publication, we use a state-of-the-art 2D feature network as a basis for 3D3L,
exploiting both intensity and depth of LiDAR range images to extract powerful
3D features. Our results show that these keypoints and descriptors extracted
from LiDAR scan images outperform state-of-the-art on different benchmark
metrics and allow for robust scan-to-scan alignment as well as global
localization.
