通过频域分析检测机器人状态估计失效，准确率超60%。
When Your State Estimator Has Lost The Plot: Detecting Estimator Failures Via Spectral Analysis
- 分析速度估计的频域功率分布，无需依赖特定传感器。
- 在三种不同系统中检测到51%-58%的已知故障，精度达60%-84%。
- 适合部署于真实机器人，可作为轻量级健康监测工具。
可靠的机载状态估计算法对机器人安全运行至关重要，但未建模干扰（如传感器混叠或分布外噪声）仍会导致估计器退化甚至完全失效。尽管已有多种方法提升鲁棒性，但能评估估计质量的内省机制仍较少。现有不确定性度量（如协方差）依赖理想假设，常过于自信；近期数据驱动方法则受限于训练数据分布。本文提出一种不依赖传感器的内省方法，通过分析近期速度估计的频域功率分布来评估估计器健康状态。在搭载视觉-惯性、激光雷达-惯性及雷达-惯性里程计的空中机器人户外飞行数据上进行验证，数据集包含多个估计器故障实例，可用于分析信号功率、谱带宽、熵等频域指标。实验发现健康与退化估计间存在一致的频域功率差异，可在三类不同状态估计框架中实现51%-58%的故障检测率，精度达60%-84%。结果表明，仅对状态估计输出进行简单频域分析，即可成为真实机器人部署中补强现有鲁棒性技术的轻量级内省工具，并为未来研究开辟新方向。
原文摘要 · Abstract (English)
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.
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