# Robust Validation to Geometric Perturbations for Autonomous Pose Estimation

arXiv: https://arxiv.org/abs/2608.21066 · HTML: https://mlsanity.com/paper/2608.21066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization](https://mlsanity.com/paper/1909.06043/) (2019)
- [On the Suitability of Lp-norms for Creating and Preventing Adversarial Examples](https://mlsanity.com/paper/1802.09653/) (2018)
- [Towards Verifying the Geometric Robustness of Large-scale Neural Networks](https://mlsanity.com/paper/2301.12456/) (2023)
- [How to design a dataset compliant with an ML-based system ODD?](https://mlsanity.com/paper/2406.14027/) (2024)
- [Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features](https://mlsanity.com/paper/2407.16223/) (2024)
- [Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP](https://mlsanity.com/paper/2505.16740/) (2025)
- [Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing System](https://mlsanity.com/paper/2605.20607/) (2026)

## Abstract (arXiv)

Deploying autonomous systems in safety-critical domains demands guaranteed robustness against physically plausible geometric perturbations rather than abstract pixel-wise noise. In vision-based navigation and autonomous landing, machine learning components require rigorous validation under dynamic operational conditions such as camera rotations and lighting shifts. Extending findings on the failure of first-order spatial attacks in classification, we show that standard gradient-based heuristics (e.g. APGD) similarly fail on for pose estimation, often performing worse than a simple random sampling baseline. To overcome these optimization bottlenecks, we reformulate pose estimation robustness within the framework of Global Lipschitzian Optimization (GLO). We argue that GLO offers a principled approach to robust validation, effectively localizing global optima with strong theoretical convergence guarantees. We evaluate this framework on a YOLOv8-Pose keypoint detector with a Perspective-n-Point (PnP) solver against rotation and contrast. In our evaluations, GLO successfully isolates critical failure modes where position deviations exceed safe operational limits, while rapidly pruning the search space by over 80%. To the best of our knowledge, this is the first study to extend geometric robustness validation to continuous keypoint regression and deep object detection, establishing a practical step toward certifying robust autonomous perception.