Does Robust VIO Need More Learning? Geometry-Verified Visual Measurements under Distribution Shift
2026-07-20 • Robotics
Robotics
AI summaryⓘ
The authors explore how much learning is needed in visual-inertial odometry (VIO) systems to remain reliable when conditions change. They propose a method that only uses learning to help find matching points between stereo images, while keeping other steps like tracking and estimation based on traditional geometry and filtering. Their approach uses uncertainty from the learned matching to improve the accuracy and stability of the system in challenging scenarios. Experiments show that combining learned matching with geometric checks works better than relying only on learned estimation.
Visual-inertial odometryStereo correspondenceUncertainty estimationGeometric verificationSliding-window estimatorTriangulationOut-of-distribution robustnessFeature matchingMotion blurDynamic scenes
Authors
Yangyang Ning, Shu Liang, Quanbo Ge, Tianchen Deng, Yuhua Qi, Shenghai Yuan
Abstract
Learning is increasingly introduced into visual-inertial odometry (VIO), ranging from learned feature front-ends to learning-dominant motion and geometry estimation. However, learning more of the pipeline does not necessarily improve robustness when deployment conditions differ from the training distribution. This work asks whether robust VIO under distribution shift truly requires deeper learned estimation, or whether learning can be confined to visual measurement generation. We propose a minimal-learning stereo VIO framework in which SEA-RAFT is used only to propose dense stereo correspondences and predict their uncertainty, while temporal tracking, geometric verification, and state estimation remain explicit. Dense flow is sampled at sparse feature locations, filtered using predicted uncertainty and stereo epipolar consistency, and incorporated into a sliding-window stereo-inertial estimator through uncertainty-weighted reprojection factors. The same uncertainty is further propagated through stereo triangulation for downstream anisotropic 3D Gaussian mapping. Experiments on EuRoC, VIODE, and 4Seasons demonstrate accurate and stable estimation under motion blur, dynamic scenes, illumination changes, and large indoor-to-outdoor distribution shifts. Ablations show that learned flow alone is insufficient: the gains arise from combining learned correspondence proposals with geometric verification and uncertainty-aware weighting. These results suggest that, for OOD-robust VIO, carefully integrated learned visual measurements can be more effective than learning a larger fraction of the estimation pipeline. Code and configs for the benchmark will be open-source upon acceptance. A supplementary video is available at https://drive.google.com/file/d/1EVRhOkhanmNXHbQS1Vr80FoEIAYOYOV2/view