Abstract
The MARWIN robot operates at the European XFEL to perform autonomous radiation monitoring in long, monotonous accelerator tunnels where conventional localization approaches struggle. Its current navigation concept combines lidar-based edge detection, wheel/lidar odometry with periodic QR-code referencing, and fuzzy control of wall distance, rotation, and longitudinal position. While robust in predefined sections, this design lacks flexibility for unknown geometries and obstacles. This paper explores deep visual stereo odometry (DVSO) with 3D-geometric constraints as a focused alternative. DVSO is purely vision-based, leveraging stereo disparity, optical flow, and self-supervised learning to jointly estimate depth and ego-motion without labeled data. For global consistency, DVSO can subsequently be fused with absolute references (e.g., landmarks) or other sensors. We provide a conceptual evaluation for accelerator tunnel environments, using the European XFEL as a case study. Expected benefits include reduced scale drift via stereo, low-cost sensing, and scalable data collection, while challenges remain in low-texture surfaces, lighting variability, computational load, and robustness under radiation. The paper defines a research agenda toward enabling MARWIN to navigate more autonomously in constrained, safety-critical infrastructures.
Abstract (translated)
MARWIN机器人在欧洲XFEL（X射线自由电子激光器）中运行，用于执行长且单调的加速器隧道中的自主辐射监测。这些地方常规定位方法难以应对。目前，MARWIN的导航概念结合了基于激光雷达的边缘检测、车轮/激光雷达里程计以及周期性二维码参考，并采用模糊控制来调节与墙壁的距离、旋转和纵向位置。尽管这种方法在预定义区域中表现稳健，但在未知几何形状和障碍物环境中缺乏灵活性。 本文探讨了一种专注于使用深度视觉立体测距（DVSO）及其3D几何约束的替代方案。DVSO是一种纯视觉方法，利用立体视差、光学流以及自我监督学习来估计深度和自身运动，无需标记数据。为了实现全局一致性，可以将DVSO与绝对参考点（如地标）或其他传感器融合。 我们使用欧洲XFEL作为案例研究，对加速器隧道环境中的这种概念进行了评估。预期的好处包括通过立体视差减少尺度漂移、低成本感应以及可扩展的数据采集能力。然而，在低纹理表面、光照变化、计算负载和辐射影响下的稳健性等问题仍待解决。 本文界定了一个研究议程，以使MARWIN能够在其受控的安全关键基础设施中实现更高级别的自主导航能力。
URL
https://arxiv.org/abs/2512.00080
