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Picometer-Precision Atomic Position Tracking through Electron Microscopy
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Elevated high-precision mapping and localization technology for periodic inspections.

Xianghua Fan1, Wenbo Pan2

  • 1Changsha University, 98 Hongshan Road, Changsha, 410022, China.

Scientific Reports
|February 9, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a new framework for precise mobile mapping in tunnels without Global Navigation Satellite System (GNSS) signals. The method accurately tracks trajectories, enabling reliable tunnel defect monitoring and assessment of progression trends.

Keywords:
Map constructionMulti-constraint FrameworkPeriodic inspectionsPositioning

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Area of Science:

  • Robotics and Autonomous Systems
  • Geomatics Engineering
  • Civil Engineering

Background:

  • Mobile mapping systems struggle with cumulative errors in tunnels due to lack of Global Navigation Satellite System (GNSS) data, hindering precise localization and map reproduction.
  • This limitation impedes accurate tunnel defect detection and the assessment of defect progression over time, crucial for infrastructure maintenance.
  • Existing multi-sensor fusion methods are often susceptible to sensor degradation, leading to trajectory estimation failures in challenging environments.

Purpose of the Study:

  • To develop a robust framework for accurate mobile mapping and trajectory estimation within tunnels, overcoming GNSS signal absence.
  • To enhance the reliability of tunnel inspection by enabling precise, repeatable point cloud mapping and defect analysis.
  • To provide a solution for long-term tunnel monitoring and defect progression assessment through accurate localization.

Main Methods:

  • A high-dimensional, multi-constraint framework integrating front-end Error-State Kalman Filter (ESKF) odometry and back-end factor graph optimization.
  • Implementation of hundred-meter marker detection and recognition for robust loop closure detection and precise initial positioning.
  • Validation through extensive experiments in real-world subway tunnel scenarios.

Main Results:

  • The proposed method demonstrates robust trajectory estimation in long tunnel scenarios, outperforming classical multi-sensor fusion techniques.
  • Achieved remarkable map trajectory consistency with accumulated errors below 0.02% in tunnel environments.
  • Exhibited high accuracy and reliability with an average positioning error of only 0.05 meters.

Conclusions:

  • The developed framework effectively eliminates accumulated errors and provides precise train positioning without GNSS, crucial for tunnel mapping.
  • The method shows significant advantages over traditional approaches, particularly in scenarios with sensor degradation.
  • Successfully validated in subway tunnels, the approach holds promising prospects for infrastructure inspection and maintenance applications.