  Robust data association is necessary for virtually every SLAM system and
finding corresponding points is typically a preprocessing step for scan
alignment algorithms. Traditionally, handcrafted feature descriptors were used
for these problems but recently learned descriptors have been shown to perform
more robustly. In this work, we propose a local feature descriptor for 3D LiDAR
scans. The descriptor is learned using a Convolutional Neural Network (CNN).
Our proposed architecture consists of a Siamese network for learning a feature
descriptor and a metric learning network for matching the descriptors. We also
present a method for estimating local surface patches and obtaining
ground-truth correspondences. In extensive experiments, we compare our learned
feature descriptor with existing 3D local descriptors and report highly
competitive results for multiple experiments in terms of matching accuracy and
computation time. \end{abstract}
