SUPER: Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry

1. 05:00
   arXiv Robotics — research abstractsSignal

   SUPER: Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry

   This research presents SUPER, a bounded controller for visual-inertial odometry that adapts visual measurement information at runtime using multi-view landmark dispersion. The system projects track consistency into pixel space and combines it with reprojection residuals to gate temporally smoothed updates. An action-verification protocol based on factor-level logs is proposed to distinguish controller-induced state changes from solver variability within nondeterministic optimizers. Evaluations were conducted in Ceres and g2o backends on EuRoC, TUM-VI, UMA-VI, and TartanAir datasets under controlled visual corruptions with a frozen policy. Results indicate that all 13 split-level confidence intervals cross zero, showing no general accuracy improvement, while effects are heterogeneous across corruption families. The controller is computationally efficient and selectively active.

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