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SUPER - Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry
arXiv cs.RO0 views
arXiv:2512.14189v2 Announce Type: replace Abstract: 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.
Source:https://arxiv.org/abs/2512.14189PreviousLearning Soft Robotic Dynamics with Active ExplorationNextA Graph-Based Conceptual Modeling Framework to Design for Human-Robot Coordination
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