  Point cloud registration is a fundamental task in many applications such as
localization, mapping, tracking, and reconstruction. Successful registration
relies on extracting robust and discriminative geometric features. Though
existing learning based methods require high computing capacity for processing
a large number of raw points at the same time, computational capacity
limitation is not an issue thanks to the powerful parallel computing process
using GPU. In this paper, we introduce a framework that efficiently and
economically extracts dense features using a graph attention network for point
cloud matching and registration (DFGAT). The detector of the DFGAT is
responsible for finding highly reliable key points in large raw data sets. The
descriptor of the DFGAT takes these keypoints combined with their neighbors to
extract invariant density features in preparation for the matching. The graph
attention network (GAT) uses the attention mechanism that enriches the
relationships between point clouds. Finally, we consider this as an optimal
transport problem and use the Sinkhorn algorithm to find positive and negative
matches. We perform thorough tests on the KITTI dataset and evaluate the
effectiveness of this approach. The results show that this method with the
efficiently compact keypoint selection and description can achieve the best
performance matching metrics and reach the highest success ratio of 99.88%
registration in comparison with other state of the art approaches.

Computer Vision and Pattern Recognition