ALLO: A Photorealistic Dataset and Data Generation Pipeline for Anomaly Detection During Robotic Proximity Operations in Lunar Orbit
<|reference_start|>ALLO: A Photorealistic Dataset and Data Generation Pipeline for Anomaly Detection During Robotic Proximity Operations in Lunar Orbit: NASA's forthcoming Lunar Gateway space station, which will be uncrewed most of the time, will need to operate with an unprecedented level of autonomy. Enhancing autonomy on the Gateway presents several unique challenges, one of which is to equip the Canadarm3, the Gateway's external robotic system, with the capability to perform worksite monitoring. Monitoring will involve using the arm's inspection cameras to detect any anomalies within the operating environment, a task complicated by the widely-varying lighting conditions in space. In this paper, we introduce the visual anomaly detection and localization task for space applications and establish a benchmark with our novel synthetic dataset called ALLO (for Anomaly Localization in Lunar Orbit). We develop a complete data generation pipeline to create ALLO, which we use to evaluate the performance of state-of-the-art visual anomaly detection algorithms. Given the low tolerance for risk during space operations and the lack of relevant data, we emphasize the need for novel, robust, and accurate anomaly detection methods to handle the challenging visual conditions found in lunar orbit and beyond.<|reference_end|>
@article{leveugle2024allo:,
    title={ALLO: A Photorealistic Dataset and Data Generation Pipeline for Anomaly
  Detection During Robotic Proximity Operations in Lunar Orbit},
    author={Selina Leveugle, Chang Won Lee, Svetlana Stolpner, Chris Langley, Paul
  Grouchy, Steven Waslander, Jonathan Kelly},
    journal={arXiv preprint arXiv:2409.20435},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20435},
    primaryClass={cs.RO}
}