ALLO: A Photorealistic Dataset and Data Generation Pipeline for Anomaly Detection During Robotic Proximity Operations in Lunar Orbit

2024-09-30

arXiv.org

AI Summary

To address external environmental monitoring requirements for the Canadarm3 robotic arm aboard NASA’s Lunar Gateway space station, this work introduces a novel visual anomaly detection and localization task tailored to extreme illumination conditions—namely, strong shadows, high dynamic range, and low signal-to-noise ratio. Methodologically, we present ALLO, the first physics-consistent, high-fidelity synthetic dataset specifically designed for lunar orbit, incorporating diverse lighting conditions, robotic arm poses, and anomaly types, augmented with realistic sensor noise; we further propose a cross-domain generalization evaluation framework. Our contributions include: (1) formally defining the first space-grade visual anomaly detection task; (2) releasing the inaugural lunar-orbit-specific benchmark; and (3) comprehensively validating our method’s robustness and pixel-level localization accuracy under stringent conditions—demonstrating significant performance gains over state-of-the-art approaches.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

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• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Detect anomalies during robotic operations in lunar orbit
• Handle varying lighting conditions for space anomaly detection
• Develop robust methods for accurate anomaly localization in space

Innovation

• Synthetic dataset ALLO for anomaly detection
• Data generation pipeline for lunar conditions
• Evaluates state-of-the-art anomaly detection algorithms