SPC-ODD: A Model-Agnostic Statistical Resilience Layer for State Estimators in Safety-Critical Autonomous Systems

Abstract

Industrial autonomous mobile robots and cyber-physical manufacturing systems increasingly rely on safety-critical state estimation pipelines; under unmodeled sensor degradation, these systems face catastrophic divergence that invalidates Operational Design Domain guarantees on the factory floor. To address this, we propose SPC-ODD (Statistical Process Control for Operational Design Domain), a deterministic, pluggable engineering protection layer combining Statistical Process Control (SPC) with kinematically adaptive gating. Model-agnostic and non-intrusive to any filter architecture, the framework employs exponentially weighted moving averages (EWMA) and cumulative sum (CUSUM) charts to bound detection latency via analytical Average Run Length (ARL) theorems, with hyperparameters $(h_{\text{drift}}=6.0, k_{\text{drift}}=0.5)$ derived from Siegmund's approximation. Evaluated across 2,000 rigorous Monte Carlo trials with highly dynamic kinematics and first-order Gauss-Markov (FOGM)-biased inertial measurement unit (IMU) physics, SPC-ODD empirically suppresses catastrophic divergence to zero within tested regimes and reduces the out-of-spec (OOS) ratio to 0.0% across all evaluated anomaly regimes. The framework executes with a computational overhead of less than $5 \mu \mathrm{s}$ per step and explicitly aligns with the auditable safety requirements of ISO 21448 (SOTIF). Cross-platform validation on the EuRoC MH_01_easy micro aerial vehicle (MAV) sequence confirms detection consistency $(\Delta t=0.804$ s) without re-tuning, demonstrating framework generalizability beyond ground AMR deployment.