状態推定器が迷走したとき：スペクトル解析による推定器故障の検出
When Your State Estimator Has Lost The Plot: Detecting Estimator Failures Via Spectral Analysis
速度推定値の周波数領域パワー分布を解析することで、状態推定器の故障をセンサ非依存で検出する手法を提案。実飛行データで3種類のオドメトリの故障を51-58%の検出率で捉えた。
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著者: Christian Lanegger, Helen Oleynikova, Roland Siegwart, Michael Pantic
分類: cs.RO
原文アブストラクト
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.
