Conceptual Evaluation of Deep Visual Stereo Odometry for the MARWIN Radiation Monitoring Robot in Accelerator Tunnels

TL;DR

This paper evaluates deep visual stereo odometry (DVSO) as a vision-based localization method for the MARWIN robot in accelerator tunnels, aiming to improve autonomous navigation in complex, safety-critical environments.

Contribution

It provides a conceptual evaluation of DVSO for tunnel environments, highlighting its potential benefits and challenges for autonomous robot navigation in radiation-prone, constrained settings.

Findings

DVSO can reduce scale drift compared to traditional methods

Stereo vision offers low-cost, scalable data collection

Challenges include low-texture surfaces and lighting variability

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.