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.

本文探索了使用分层注意机制和图神经网络的学习模型来替代传统的几何配准和姿态图优化的框架，通过压缩数据流并保留估计刚性姿态所需的基本信息，大大提高了姿态估计的精确性，尤其在旋转组件方面的改进较为明显。