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Automated Point Cloud Registration Approach Optimized for a Stop-and-Go Scanning System.

Sangyoon Park1, Sungha Ju1, Minh Hieu Nguyen1

  • 1Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated point cloud registration method for mobile robots, significantly improving efficiency and accuracy in terrestrial laser scanning. The novel approach ensures 100% successful scan registration in real-world indoor environments.

Keywords:
point cloud registrationstop-and-go scanning systemsterrestrial laser scanning

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Area of Science:

  • Robotics and Automation
  • Geomatics Engineering
  • Computer Vision

Background:

  • Terrestrial laser scanning (TLS) advances enable automatic data acquisition, but registration remains a manual bottleneck.
  • Mobile robots, particularly quadruped walking robots, offer platforms for stop-and-go scanning systems.
  • Automated registration is crucial for efficient processing of large-scale point cloud data.

Purpose of the Study:

  • To develop and validate an automated point cloud registration approach for stop-and-go scanning systems.
  • To overcome the limitations of manual registration in terrestrial laser scanning workflows.
  • To enhance the efficiency and reliability of processing scan data from mobile robotic platforms.

Main Methods:

  • Perpendicular constrained wall-plane extraction for initial feature identification.
  • Coarse registration using plane matching and point-to-point displacement calculation.
  • Fine registration employing horizontality constrained Iterative Closest Point (ICP) algorithm.

Main Results:

  • Achieved automated registration with high accuracy (0.044 m).
  • Demonstrated a 100% successful scan rate (SSR) for 18 scan datasets.
  • Completed registration within 424.2 seconds in a real-world indoor environment.
  • Outperformed conventional methods, especially for point cloud pairs with low overlap.

Conclusions:

  • The proposed automated registration method is effective for stop-and-go scanning systems on quadruped robots.
  • This approach significantly reduces manual intervention and processing time in point cloud data.
  • It offers reliable registration performance under challenging indoor conditions with limited data overlap.