---
格式版本: 2
标题: "When Your State Estimator Has Lost The Plot: Detecting Estimator Failures Via Spectral Analysis"
原文链接: "https://arxiv.org/abs/2608.10623"
发布日期: "2026-08-11"
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发现时间: "2026-08-12T11:13:54+08:00"
入库时间: "2026-08-12T03:17:07.275Z"
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去重键: "https://arxiv.org/abs/2608.10623"
---

## Computer Science > Robotics

## Title:When Your State Estimator Has Lost The Plot: Detecting Estimator Failures Via Spectral Analysis

[View PDF](https://arxiv.org/pdf/2608.10623) [HTML (experimental)](https://arxiv.org/html/2608.10623v1)

> 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.

| Subjects: | Robotics (cs.RO) |
| --- | --- |
| Cite as: | [arXiv:2608.10623](https://arxiv.org/abs/2608.10623) \[cs.RO\] |
|  | (or [arXiv:2608.10623v1](https://arxiv.org/abs/2608.10623v1) \[cs.RO\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10623](https://doi.org/10.48550/arXiv.2608.10623) |

## Submission history

From: Christian Lanegger \[[view email](https://arxiv.org/show-email/4a3a7329/2608.10623)\]  
**\[v1\]** Tue, 11 Aug 2026 08:08:53 UTC (4,773 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10623) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
