  Accurate, robust, and real-time LiDAR-based odometry (LO) is imperative for
many applications like robot navigation, globally consistent 3D scene map
reconstruction, or safe motion-planning. Though LiDAR sensor is known for its
precise range measurement, the non-uniform and uncertain point sampling density
induce structural inconsistencies. Hence, existing supervised and unsupervised
point set registration methods fail to establish one-to-one matching
correspondences between LiDAR frames. We introduce a novel deep learning-based
real-time (approx. 35-40ms per frame) LO method that jointly learns accurate
frame-to-frame correspondences and model's predictive uncertainty (PU) as
evidence to safe-guard LO predictions. In this work, we propose (i) partial
optimal transportation of LiDAR feature descriptor for robust LO estimation,
(ii) joint learning of predictive uncertainty while learning odometry over
driving sequences, and (iii) demonstrate how PU can serve as evidence for
necessary pose-graph optimization when LO network is either under or over
confident. We evaluate our method on KITTI dataset and show competitive
performance, even superior generalization ability over recent state-of-the-art
approaches. Source codes are available.
