Problem
Research questions and friction points this paper is trying to address.
state estimation failure
estimator introspection
spectral analysis
robustness
uncertainty assessment
When Your State Estimator Has Lost The Plot: Detecting Estimator Failures Via Spectral Analysis
🤖 AI Summary
This work addresses the vulnerability of state estimators to unmodeled disturbances—such as sensor aliasing or out-of-distribution noise—and their lack of a general self-assessment mechanism. To this end, the authors propose a sensor-agnostic introspective method that, for the first time, leverages frequency-domain spectral analysis for generic estimator health monitoring. By examining power spectral characteristics—such as signal power, bandwidth, and entropy—of recent velocity estimates, the approach operates without reliance on specific sensor models or assumptions about training data distributions. Experimental results demonstrate that this lightweight method is effective across diverse visual-inertial, LiDAR-inertial, and radar-inertial odometry systems, achieving 51%–58% fault recall and 60%–84% precision on real-world flight datasets, thereby validating the discriminative power of spectral features in detecting estimation failure.
📝 Abstract
Reliable onboard state estimation is essential for safe robotic operation, yet unmodeled disturbances, such as sensor aliasing or out-of-distribution noise, still cause estimators to degrade or fail completely. While many methods aim to improve estimator robustness, only a few provide introspective mechanisms to assess estimate quality. Existing uncertainty measures, such as covariances, rely on idealized assumptions and tend to be overconfident, and more recent data-driven approaches are typically tied to their training data distributions.
We propose a sensor-agnostic introspective method that assesses estimator health by analyzing the frequency-domain power distribution of recent velocity estimates. The method is evaluated using outdoor flight data from an aerial robot running visual-inertial, LiDAR-inertial, and radar-inertial odometry. The dataset includes multiple estimator failures, enabling analysis of several frequency-domain indicators, such as signal power, spectral bandwidth, and entropy. We observe consistent spectral power differences between healthy and degraded estimates, allowing detection of 51%-58% of labeled failures with 60%-84% precision across three fundamentally different state estimation frameworks. Our results show that even a simple frequency-domain analysis of a state estimator's output can serve as a lightweight introspective tool to complement existing robustness techniques in real-world robotic deployments, and opens promising avenues for future investigation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
spectral analysis
state estimator failure detection
frequency-domain introspection
sensor-agnostic monitoring
robotic state estimation
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