A key component of Visual Simultaneous Localization and Mapping (VSLAM) is estimating relative camera poses using matched keypoints. Accurate estimation is challenged by noisy correspondences. Classical methods rely on stochastic hypothesis sampling and iterative estimation, while learning-based methods often lack explicit geometric structure. In this work, we reformulate relative pose estimation as a relational inference problem over epipolar correspondence graphs, where matched keypoints are nodes and nearby ones are connected by edges. Graph operations such as pruning, message passing, and pooling estimate a quaternion rotation, translation vector, and the Essential Matrix (EM). Minimizing a loss comprising (i) $\mathcal{L}_2$ differences with ground truth (GT), (ii) Frobenius norm between estimated and GT EMs, (iii) singular value differences, (iv) heading angle differences, and (v) scale differences, yields the relative pose between image pairs. The dense detector-free method LoFTR is used for matching. Experiments on indoor and outdoor benchmarks show improved robustness to dense noise and large baseline variation compared to classical and learning-guided approaches, highlighting the effectiveness of global relational consensus.

翻译：视觉同时定位与地图构建（VSLAM）的核心环节是利用匹配关键点估计相对相机位姿。然而，噪声对应关系会严重降低估计的准确性。传统方法依赖随机假设采样与迭代估计，而基于学习的方法往往缺乏明确的几何结构。本文重新将相对位姿估计表述为极线对应图上的关系推理问题：匹配关键点作为节点，近邻点通过边连接。通过图操作（剪枝、消息传递与池化）可估计四元数旋转、平移向量及本质矩阵（EM）。最小化包含以下分量的损失函数：（i）与真值（GT）的 $\mathcal{L}_2$ 差异；（ii）估计EM与GT EM的Frobenius范数误差；（iii）奇异值差异；（iv）航向角差异；（v）尺度差异，即可获得图像对间的相对位姿。匹配采用无检测器的密集方法LoFTR。室内外基准测试实验表明，相较于传统方法与学习型方法，该方法对密集噪声和大基线变化具有更强的鲁棒性，充分验证了全局关系共识的有效性。