Protection Levels for Vision-Based Pose Estimation
arXiv:2608.10023v1 Announce Type: new Abstract: Vision-based navigation complements Global Navigation Satellite Systems, but certification demands integrity guarantees that account for faulty measurements. Previous work presented a probabilistic computer vision pipeline for runway-based pose estimation with fault detection inspired by Receiver Autonomous Integrity Monitoring. This work extends that framework by deriving protection levels, which provide probabilistic bounds on pose error that remain valid under undetected faults. We present an algorithm for computing protection levels for the nonlinear Perspective-$n$-Point problem applied to an aviation setting. The algorithm covers all six degrees of freedom of the aircraft pose (position and orientation) directly. We analyze the effect of measurement redundancy, pixel-level prediction uncertainty, and runway distance on the resulting protection levels. To make the results tangible, we demonstrate tradeoffs in the protection levels on an illustrative runway example.
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[Submitted on 9 Aug 2026]
Title:Protection Levels for Vision-Based Pose Estimation
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Abstract:Vision-based navigation complements Global Navigation Satellite Systems, but certification demands integrity guarantees that account for faulty measurements. Previous work presented a probabilistic computer vision pipeline for runway-based pose estimation with fault detection inspired by Receiver Autonomous Integrity Monitoring. This work extends that framework by deriving protection levels, which provide probabilistic bounds on pose error that remain valid under undetected faults. We present an algorithm for computing protection levels for the nonlinear Perspective-$n$-Point problem applied to an aviation setting. The algorithm covers all six degrees of freedom of the aircraft pose (position and orientation) directly. We analyze the effect of measurement redundancy, pixel-level prediction uncertainty, and runway distance on the resulting protection levels. To make the results tangible, we demonstrate tradeoffs in the protection levels on an illustrative runway example.
Comments: 11 pages, 5 figures. Accepted for publication at the 2026 AIAA DATC/IEEE 45th Digital Avionics Systems Conference (DASC). O. Beyer Bruvik and R. Valentin contributed equally
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Systems and Control (eess.SY)
Cite as: arXiv:2608.10023 [cs.RO]
(or arXiv:2608.10023v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.10023
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Olivia Beyer Bruvik [view email] [v1] Sun, 9 Aug 2026 12:37:39 UTC (106 KB)
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