Protection Levels for Vision-Based Pose Estimation

• URL: http://arxiv.org/abs/2608.10023v1
• Date: Sun, 09 Aug 2026 12:37:39 GMT
• Title: Protection Levels for Vision-Based Pose Estimation
• Abstract summary: We present an algorithm for computing protection levels for a nonlinear Perspective-$n$-Point problem applied to an aviation setting.<n>We analyze the effect of measurement redundancy, pixel-level prediction uncertainty, and runway distance on the resulting protection levels.
• Score: 20.19556123524161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels [53.63220028820581]
  Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction.<n>We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably.
  arXiv  Detail & Related papers  (2026-07-28T17:50:44Z)
• Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents [58.49879338545782]
  Long-horizon tasks are common in real-world robotic deployments, yet failure detection for such tasks remains underexplored.<n>We present Foresight, a failure detection framework that monitors manipulation trajectories using latent representations from an action-conditioned world model.<n>Our results suggest that action-conditioned world-model embeddings provide a scalable representation for reliable failure monitoring in long-horizon manipulation.
  arXiv  Detail & Related papers  (2026-06-22T09:32:28Z)
• A UAV-Based Multi-Modal Vision System for Automated Sideslope Deformation Monitoring and Hazard Detection [22.798047516914185]
  This study aims to develop a highly automated workflow for slope hazard detection using Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR)<n>The proposed workflow consists of a shared data-acquisition and ground-surface extraction stage, a single-observation hazard-screening branch based on RandLA-Net, and a multi-epoch deformation-monitoring branch based on grid-wise elevation differencing.<n>The results show that the workflow can extract usable ground-surface point clouds under vegetation cover, identify potential hazard zones from single-observation point clouds, and quantify centimeter-level elevation changes using multi
  arXiv  Detail & Related papers  (2026-06-13T19:20:49Z)
• Evidence-Based Landing Site Selection and Vison-Based Landing for UAVs in Unstructured Environments [0.9236074230806578]
  This paper presents an evidence-based probabilistic framework for autonomous UAV landing.<n>It explicitly separates decision-making under uncertainty from execution via visual servoing.<n>It is validated through both real-world laboratory experiments and high-fidelity simulations in Nvidia Isaac Sim.
  arXiv  Detail & Related papers  (2026-05-02T13:05:20Z)
• Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System [32.06417142452719]
  We present a vision-based pipeline for aircraft pose estimation from runway images.<n>Our approach features three key innovations: (i) an efficient, flexible neural architecture based on a spatial Soft Argmax operator for probabilistic keypoint regression, supporting diverse vision backbones with real-time inference; (ii) a principled loss function producing predictive uncertainties, which are evaluated via sharpness and calibration metrics; and (iii) an adaptation of Residual-based Receiver Autonomous Integrity Monitoring (RAIM) enabling runtime detection and rejection of faulty model outputs.
  arXiv  Detail & Related papers  (2025-08-13T11:56:22Z)
• Verification of Visual Controllers via Compositional Geometric Transformations [49.81690518952909]
  We introduce a novel verification framework for perception-based controllers that can generate outer-approximations of reachable sets.<n>We provide theoretical guarantees on the soundness of our method and demonstrate its effectiveness across benchmark control environments.
  arXiv  Detail & Related papers  (2025-07-06T20:22:58Z)
• Conformal Safety Shielding for Imperfect-Perception Agents [7.5422935754618825]
  We consider the problem of safe control in discrete autonomous agents that use learned components for imperfect perception.<n>We propose a shield construction that provides run-time safety guarantees under perception errors.
  arXiv  Detail & Related papers  (2025-06-12T17:37:29Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Approximate Shielding of Atari Agents for Safe Exploration [83.55437924143615]
  We propose a principled algorithm for safe exploration based on the concept of shielding. We present preliminary results that show our approximate shielding algorithm effectively reduces the rate of safety violations.
  arXiv  Detail & Related papers  (2023-04-21T16:19:54Z)
• USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving [7.355977594790584]
  We consider the safety-oriented performance of 3D object detectors in autonomous driving contexts.<n>We present uncompromising spatial constraints (USC), which characterize a simple yet important localization requirement.<n>We incorporate the quantitative measures into common loss functions to enable safety-oriented fine-tuning for existing models.
  arXiv  Detail & Related papers  (2022-09-21T14:03:08Z)
• Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving [77.39239190539871]
  We show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving. We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function. We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods.
  arXiv  Detail & Related papers  (2021-05-28T09:23:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.