Perception with Guarantees: Certified Pose Estimation via Reachability Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918499190833152 |
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| author | Ladner, Tobias Shoukry, Yasser Althoff, Matthias |
| author_facet | Ladner, Tobias Shoukry, Yasser Althoff, Matthias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10032 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Perception with Guarantees: Certified Pose Estimation via Reachability Analysis Ladner, Tobias Shoukry, Yasser Althoff, Matthias Computer Vision and Pattern Recognition Robotics 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. |
| title | Perception with Guarantees: Certified Pose Estimation via Reachability Analysis |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2602.10032 |