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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Laser SLAM Matching Localization Method for Subway Tunnel Point Clouds.

Yi Zhang¹, Feiyang Dong¹, Qihao Sun¹

¹School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)

|June 27, 2025

Summary

This summary is machine-generated.

Accurate simultaneous localization and mapping (SLAM) in tunnels is challenging. This study introduces a novel coarse-to-fine registration strategy, improving SLAM accuracy and efficiency in geometrically similar environments like subway tunnels.

Keywords:

SLAM feature extraction registration subway tunnels

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Area of Science:

Robotics
Computer Vision
Geospatial Analysis

Background:

Scan-Map registration in geometrically similar environments, such as subway tunnels, is sensitive to initial pose values, often leading to mismatches.
This limitation restricts the application of Simultaneous Localization and Mapping (SLAM) in tunnel navigation.

Purpose of the Study:

To develop a robust coarse-to-fine registration strategy for enhancing SLAM performance in tunnel environments.
To improve the accuracy and efficiency of pose estimation for lidar-based SLAM.

Main Methods:

A novel strategy combining geometric feature extraction (point distance, line, plane, convex hull) and a keyframe-based pose optimization model.
Coarse registration using extracted features followed by fine registration with Point-Plane ICP, utilizing the coarse pose as initialization.
Validation using Innovusion lidar scans from a subway tunnel environment.

Main Results:

Achieved a single-frame point cloud registration accuracy of 3 cm.
Demonstrated high efficiency with registration completed within 0.7 seconds.
Significantly outperformed traditional registration algorithms in accuracy and speed.

Conclusions:

The proposed coarse-to-fine registration method effectively addresses the challenges of SLAM in tunnel environments.
The strategy enhances the applicability of SLAM by ensuring high registration accuracy and efficiency.
This advancement is crucial for reliable navigation and mapping in complex, repetitive structures.