AI 中文总结
本研究提出一种与传感器无关的频域分析方法，用于检测状态估计器故障，在三类里程计框架中可检测51%-58%的故障，精度达60%-84%，可作为轻量级自省工具。
AI 中文摘要
可靠的机载状态估计是机器人安全运行的关键，但传感器混叠、分布外噪声等未建模扰动仍会导致估计器性能下降或完全失效。尽管许多方法旨在提升估计器的鲁棒性，却仅有少数具备评估估计质量的自省机制。现有不确定性度量（如协方差）依赖理想化假设，往往过于自信；而较新的数据驱动方法通常与训练数据分布绑定。我们提出一种与传感器无关的自省方法，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。该方法使用空中机器人的户外飞行数据进行评估，该机器人运行视觉-惯性、激光雷达-惯性、雷达-惯性里程计。数据集包含多次估计器故障，可用于分析信号功率、频谱带宽、熵等多种频域指标。我们观察到正常与退化估计之间存在一致的频谱功率差异，在三种完全不同的状态估计框架中，能够以60%-84%的精度检测51%-58%的标记故障。我们的结果表明，即使对状态估计器的输出进行简单的频域分析，也可作为轻量级自省工具，补充现实世界机器人部署中现有的鲁棒性技术，并为未来研究开辟了有前景的方向。
英文摘要
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.
