Addressing camera sensors faults in Vision-Based Navigation: Simulation and dataset development

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. 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 1 1 The link will be available upon publication. For the time being, readers are kindly requested to contact the corresponding author to obtain access to the dataset. is available. • We describe failure modes applicable to the interplanetary exploration missions Astrone KI • We provide a useful engineering guide for future missions employing camera sensors • We show camera fault injection strategies in a simulated comet scenario • Implementation details and relevant parameters for the fault injection simulation are given • A novel synthetic faulty image dataset is proposed to support AI algorithm training