  Reliable robot pose estimation is a key building block of many robot autonomy
pipelines, with LiDAR localization being an active research domain. In this
work, a versatile self-supervised LiDAR odometry estimation method is
presented, in order to enable the efficient utilization of all available LiDAR
data while maintaining real-time performance. The proposed approach selectively
applies geometric losses during training, being cognizant of the amount of
information that can be extracted from scan points. In addition, no labeled or
ground-truth data is required, hence making the presented approach suitable for
pose estimation in applications where accurate ground-truth is difficult to
obtain. Furthermore, the presented network architecture is applicable to a wide
range of environments and sensor modalities without requiring any network or
loss function adjustments. The proposed approach is thoroughly tested for both
indoor and outdoor real-world applications through a variety of experiments
using legged, tracked and wheeled robots, demonstrating the suitability of
learning-based LiDAR odometry for complex robotic applications.
