用新型神经网络提升月球着陆视觉定位精度与稳定性。
Learning to Anchor Visual Odometry: KAN-Based Pose Regression for Planetary Landing
- 用KAN网络学习图像特征到全局坐标映射，生成可靠姿态锚点。
- 在真实与合成数据上，平移误差降32%，旋转误差降45%。
- 适合需要高精度实时定位的太空着陆任务，抗遮挡能力强。
精确且实时的6-DoF定位对自主月球着陆至关重要，但现有方法受限：视觉里程计（VO）存在无界漂移，基于地图的绝对定位在纹理稀疏或低光照地形下失效。我们提出KANLoc，一种单目定位框架，将VO与轻量但鲁棒的绝对位姿回归器紧密耦合。核心为科尔莫戈罗夫-阿诺德网络（KAN），学习从图像特征到地图坐标的复杂映射，生成稀疏但高度可靠的全局姿态锚点。这些锚点被融合进束调整框架，有效消除漂移同时保持局部运动精度。KANLoc实现三大改进：(i) 基于KAN的位姿回归器，在参数效率上表现优异；(ii) 混合VO-绝对定位方案，实现实时全局一致轨迹（≥15 FPS）；(iii) 针对传感器遮挡设计的数据增强策略，提升鲁棒性。在真实与合成月球着陆数据集上，平均平移误差降低32%，旋转误差降低45%，单轨迹最高提升达45%/48%，优于强基线。
原文摘要 · Abstract (English)
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.
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