  The most commonly used method for addressing 3D geometric registration is the
iterative closet-point algorithm, this approach is incremental and prone to
drift over multiple consecutive frames. The Common strategy to address the
drift is the pose graph optimization subsequent to frame-to-frame registration,
incorporating a loop closure process that identifies previously visited places.
In this paper, we explore a framework that replaces traditional geometric
registration and pose graph optimization with a learned model utilizing
hierarchical attention mechanisms and graph neural networks. We propose a
strategy to condense the data flow, preserving essential information required
for the precise estimation of rigid poses. Our results, derived from tests on
the KITTI Odometry dataset, demonstrate a significant improvement in pose
estimation accuracy. This improvement is especially notable in determining
rotational components when compared with results obtained through conventional
multi-way registration via pose graph optimization. The code will be made
available upon completion of the review process.
