arXiv:2512.14189 · v2
SUPER - Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry
摘要
This paper presents SUPER (Sensitivity-based Uncertainty-aware PErformance and Risk assessment), a bounded controller that adapts visual measurement information in visual-inertial odometry at runtime. Building on multi-view landmark dispersion from stereo observations, SUPER projects track consistency into pixel space and combines it with the reprojection residual to gate a temporally smoothed, bounded information update. Because the controller operates inside nondeterministic optimizers, we additionally propose an action-verification protocol based on factor-level logs, inactive-instrumented controls, and same-linearization replay. We evaluate SUPER in Ceres and g2o backends on EuRoC, TUM-VI, UMA-VI, and TartanAir under controlled visual corruptions, with a frozen policy and prospective confirmation. SUPER is computationally efficient and selectively active, but does not establish a general accuracy improvement: all 13 split-level confidence intervals cross zero, and effects are heterogeneous across corruption families. The action-verification protocol reliably distinguishes controller-induced state changes from solver variability.
作者备注：6 pages, 4 figures, 5 tables. v2: substantially revised. The Schur-complement risk indicator of v1 is replaced by a closed-loop controller adapting visual information from multi-view track dispersion, with a new action-verification protocol. ISO risk mapping and the prediction/stop-policy results of v1 are removed. v2 claims no general accuracy improvement