# Perception with Guarantees: Certified Pose Estimation via Reachability Analysis

arXiv: https://arxiv.org/abs/2602.10032 · HTML: https://mlsanity.com/paper/2602.10032/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 21 of 21)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [TEASER: Fast and Certifiable Point Cloud Registration](https://mlsanity.com/paper/2001.07715/) (2020)
- [A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers](https://mlsanity.com/paper/2207.00254/) (2022)
- [Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation](https://mlsanity.com/paper/2303.12246/) (2023)
- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Results](https://mlsanity.com/paper/2512.19007/) (2025)
- [Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopes](https://mlsanity.com/paper/2207.02715/) (2022)
- [NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing](https://mlsanity.com/paper/2203.15841/) (2022)
- [Sparse Polynomial Zonotopes: A Novel Set Representation for Reachability Analysis](https://mlsanity.com/paper/1901.01780/) (2019)
- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [Certifiable 3D Object Pose Estimation: Foundations, Learning Models, and Self-Training](https://mlsanity.com/paper/2206.11215/) (2022)
- [Certifiable Relative Pose Estimation](https://mlsanity.com/paper/2003.13732/) (2020)
- [Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure](https://mlsanity.com/paper/2404.15065/) (2024)
- [Out of the Shadows: Exploring a Latent Space for Neural Network Verification](https://mlsanity.com/paper/2505.17854/) (2025)
- [Certified Vision-based State Estimation for Autonomous Landing Systems using Reachability Analysis](https://mlsanity.com/paper/2309.05167/) (2023)
- [Certifying Robustness of Learning-Based Keypoint Detection and Pose Estimation Methods](https://mlsanity.com/paper/2408.00117/) (2024)
- [Distributed Set-Based Observers Using Diffusion Strategies](https://mlsanity.com/paper/2003.10347/) (2020)
- [Correct-by-Construction Vision-based Pose Estimation using Geometric Generative Models](https://mlsanity.com/paper/2601.17556/) (2026)

## Abstract (arXiv)

Agents in cyber-physical systems are increasingly entrusted with safety-critical tasks. Ensuring safety of these agents often requires localizing the pose for subsequent actions. Pose estimates can, e.g., be obtained from various combinations of lidar sensors, cameras, and external services such as GPS. Crucially, in safety-critical domains, a rough estimate is insufficient to formally determine safety, i.e., guaranteeing safety even in the worst-case scenario, and external services might additionally not be trustworthy. We address this problem by presenting a certified pose estimation in 3D solely from a camera image and a well-known target geometry. This is realized by formally bounding the pose, which is computed by leveraging recent results from reachability analysis and formal neural network verification. Our experiments demonstrate that our approach efficiently and accurately localizes agents in both synthetic and real-world experiments.