Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Development

• URL: http://arxiv.org/abs/2507.02602v1
• Date: Thu, 03 Jul 2025 13:23:22 GMT
• Title: Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Development
• Authors: Riccardo Gallon, Fabian Schiemenz, Alessandra Menicucci, Eberhard Gill,
• Abstract summary: This study focuses on an interplanetary exploration mission scenario.<n>A comprehensive analysis of potential fault cases in camera sensors used within the VBN pipeline is presented.<n>A simulation framework is introduced to recreate faulty conditions in synthetically generated images, enabling a systematic and controlled reproduction of faulty data.<n>The resulting dataset of fault-injected images provides a valuable tool for training and testing AI-based fault detection algorithms.
• Score: 41.94295877935867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing importance of Vision-Based Navigation (VBN) algorithms in space missions raises numerous challenges in ensuring their reliability and operational robustness. Sensor faults can lead to inaccurate outputs from navigation algorithms or even complete data processing faults, potentially compromising mission objectives. Artificial Intelligence (AI) offers a powerful solution for detecting such faults, overcoming many of the limitations associated with traditional fault detection methods. However, the primary obstacle to the adoption of AI in this context is the lack of sufficient and representative datasets containing faulty image data. This study addresses these challenges by focusing on an interplanetary exploration mission scenario. A comprehensive analysis of potential fault cases in camera sensors used within the VBN pipeline is presented. The causes and effects of these faults are systematically characterized, including their impact on image quality and navigation algorithm performance, as well as commonly employed mitigation strategies. To support this analysis, a simulation framework is introduced to recreate faulty conditions in synthetically generated images, enabling a systematic and controlled reproduction of faulty data. The resulting dataset of fault-injected images provides a valuable tool for training and testing AI-based fault detection algorithms. The final link to the dataset will be added after an embargo period. For peer-reviewers, this private link is available.

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