学习锚定视觉里程计：基于KAN的行星着陆姿态回归
Learning to Anchor Visual Odometry: KAN-Based Pose Regression for Planetary Landing
AI总结：
KANLoc通过基于KAN的姿态回归器和混合定位方案，实现高精度、实时的行星着陆定位，有效减少漂移并提升鲁棒性。
摘要：
准确且实时的6自由度定位对于自主月球着陆至关重要，但现有方法仍存在局限：视觉里程计（VO）会无限制漂移，而基于地图的绝对定位在纹理稀疏或低光地形中失效。我们引入KANLoc，一种单目定位框架，紧密耦合VO与轻量但稳健的绝对姿态回归器。其核心是一个Kolmogorov-Arnold网络（KAN），学习从图像特征到地图坐标的复杂映射，产生稀疏但高度可靠的全局姿态锚点。这些锚点被融合到束调整框架中，有效消除漂移同时保留局部运动精度。KANLoc实现了三个关键进展：（i）基于KAN的姿态回归器，以显著的参数效率实现高精度；（ii）混合VO-绝对定位方案，产生一致的实时轨迹（>=15 FPS）；（iii）定制的数据增强策略，提高对传感器遮挡的鲁棒性。在现实合成和真实月球着陆数据集上，KANLoc将平均平移和旋转误差分别减少32%和45%，单轨迹收益最高达45%/48%，优于强大基线。
英文摘要：
Accurate and real-time 6-DoF localization is mission-critical for autonomous lunar landing, yet existing approaches remain limited: visual odometry (VO) drifts unboundedly, while map-based absolute localization fails in texture-sparse or low-light terrain. We introduce KANLoc, a monocular localization framework that tightly couples VO with a lightweight but robust absolute pose regressor. At its core is a Kolmogorov-Arnold Network (KAN) that learns the complex mapping from image features to map coordinates, producing sparse but highly reliable global pose anchors. These anchors are fused into a bundle adjustment framework, effectively canceling drift while retaining local motion precision. KANLoc delivers three key advances: (i) a KAN-based pose regressor that achieves high accuracy with remarkable parameter efficiency, (ii) a hybrid VO-absolute localization scheme that yields globally consistent real-time trajectories (>=15 FPS), and (iii) a tailored data augmentation strategy that improves robustness to sensor occlusion. On both realistic synthetic and real lunar landing datasets, KANLoc reduces average translation and rotation error by 32% and 45%, respectively, with per-trajectory gains of up to 45%/48%, outperforming strong baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
