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.

翻译：最常用的三维几何配准方法是迭代最近点算法，该方法采用增量式处理，在多连续帧中易产生漂移。常见的漂移抑制策略是在帧间配准之后进行位姿图优化，通过闭环检测识别先前访问的位置。本文探索了一种框架，用带有层次注意力机制和图神经网络的深度学习模型替代传统几何配准与位姿图优化。我们提出一种数据流压缩策略，保留刚体变换精确估计所需的关键信息。在KITTI里程计数据集上的测试结果表明，该方法显著提升了位姿估计精度，尤其是在旋转分量的确定上，相较于通过位姿图优化的传统多路配准方法优势更为突出。代码将在审稿流程完成后公开。