Learning to Anchor Visual Odometry: KAN-Based Pose Regression for Planetary Landing

TL;DR

This paper presents KANLoc, a novel monocular localization framework combining visual odometry with a lightweight pose regressor based on Kolmogorov-Arnold Networks, achieving high accuracy and robustness for lunar landing missions.

Contribution

Introduction of KANLoc, a hybrid VO-absolute localization system utilizing KAN for reliable global pose anchors, improving accuracy and robustness in challenging lunar terrains.

Findings

Reduces translation error by 32%

Reduces rotation error by 45%

Operates at >=15 FPS with high accuracy

Abstract

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.