深度视觉立体视觉里程计的概念评估：用于加速器隧道中MARWIN辐射监测机器人的评估
Conceptual Evaluation of Deep Visual Stereo Odometry for the MARWIN Radiation Monitoring Robot in Accelerator Tunnels
AI总结：
本文评估了深度视觉立体视觉里程计在加速器隧道中为MARWIN机器人提供自主导航的可能性，通过结合3D几何约束，旨在提高在未知环境中的鲁棒性和灵活性。
摘要：
MARWIN机器人在欧洲XFEL运行，用于在长而单调的加速器隧道中进行自主辐射监测，其中传统定位方法面临困难。其当前导航概念结合了基于激光雷达的边缘检测、轮/激光雷达里程计与周期性二维码参考，以及对墙壁距离、旋转和纵向位置的模糊控制。虽然在预定义区域内具有鲁棒性，但该设计在未知几何形状和障碍物面前缺乏灵活性。本文探讨了深度视觉立体视觉里程计（DVSO）结合3D几何约束作为替代方案。DVSO是纯视觉方法，利用立体视差、光流和自监督学习联合估计深度和自身运动，无需标注数据。为了实现全局一致性，DVSO可以随后与绝对参考（如地标）或其他传感器融合。我们为加速器隧道环境提供了概念评估，以欧洲XFEL为案例研究。预期的益处包括通过立体视差减少比例漂移、低成本传感和可扩展的数据收集，同时仍存在低纹理表面、光照变化、计算负载和辐射下的鲁棒性等挑战。本文定义了一个研究议程，旨在使MARWIN能够在受限、安全关键的基础设施中实现更自主的导航。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
