arXiv:2611.05938 · v1
Reflection-Robust Stereo Visual-Inertial Odometry via Kalman-Filtered Ground-Height Estimation
摘要
Visual-inertial odometry relies on visual features that correspond to 3D points with consistent projections across sequential frames. In reflective indoor-floor environments, however, specular floor patterns can be detected and triangulated as virtual features below the physical ground plane, producing unreliable feature associations and inconsistent visual residuals. This paper proposes a lightweight reflection-aware front-end for feature-based stereo VIO. Rather than suppressing all floor-region features, the proposed method estimates the ground height in real time and rejects only feature candidates whose inferred 3D positions are below the estimated ground height. A floor-region mask restricts the validation process to reflection-prone regions, while stereo height uncertainty and pose uncertainty are used to construct frame-level ground-height measurements. A scalar Kalman filter with Normalized Innovation Squared (NIS) gating then provides a stable ground-height estimate, and an uncertainty-aware safety margin is used for selective below-ground virtual feature rejection. Experiments on real reflective-floor indoor sequences show that the proposed front-end generally improves trajectory accuracy in reflection-dominant environments while maintaining accurate ground-height estimates and recovering from actual ground-height changes.
作者备注：6 pages, 5 figures, 3 tables. Presented at the 26th International Conference on Control, Automation, and Systems (ICCAS 2026), Sapporo, Japan. Included in the conference proceedings