提出新方法在位置不确定下实现亚度级航向估计，提升跨视角定位精度。
Cross-View Yaw Estimation in Location Uncertainty with Line-Aligning Yaw Scoring
- 基于径向不变性设计投票机制，分离航向与位置误差
- 在多个数据集上未知航向时提升28%~45%定位准确率
- 适合需要高精度航向估计的自动驾驶与地图构建场景
精准的航向估计是地表视图与鸟瞰图（BEV）间跨视角定位的关键瓶颈。现有方法将航向与平移耦合，依赖高度或投影假设，在大航向模糊下性能下降。本文解耦航向与定位精度，提出LAYS——一种径向不变的线一致性投票方法。通过利用公式的径向不变性，对所有候选姿态进行3D投票，实现亚度级航向精度，且无需精确位置信息。核心观察是：地表图像列与BEV像素匹配时，沿像素径向方向各相机位置产生的航向一致。LAYS使用特征相似性匹配BEV像素与地表列，将诱导的航向投票累积至离散3D桶中，正确对应关系沿径向线集中形成锐峰，对应真实航向。在Mapillary、Ford、KITTI和VIGOR数据集上的实验表明，当航向未知时显著提升性能，尤其在正常视场角下航向未知时提升28%~45%；将LAYS作为航向先验可改善下游3-DoF定位效果。
原文摘要 · Abstract (English)
Accurate yaw estimation is a bottleneck in cross-view localization between ground view and Bird's Eye View (BEV). Existing methods couple yaw with translation and rely on height or projection assumptions that degrade under large yaw ambiguity. We disentangle yaw from location accuracy and introduce LAYS, a radially invariant line-consensus voting method. By exploiting the radial invariance of our formulation, we achieve sub-degree yaw precision via 3D voting over all candidate poses, while eliminating the need for accurate location. Our key observation is that a ground-image column matched to BEV pixels induces the same yaw across all camera positions along the radial direction of the pixels. LAYS matches BEV pixels to ground columns using feature similarity and accumulates the induced yaw votes into discrete 3D bins, where correct correspondences along the radial line concentrate into a sharp peak for the correct yaw. Experiments on Mapillary, Ford, KITTI, and VIGOR show significant gains under unknown yaw, particularly for normal FoV with unknown yaw (+28$\sim$45\%p), and using LAYS as a yaw prior improves downstream 3-DoF localization.
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