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Study on TLS Point Cloud Registration Algorithm for Large-Scale Outdoor Weak Geometric Features.

Chen Li1, Yonghua Xia2, Minglong Yang2

  • 1Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.

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Summary

This study enhances outdoor terrestrial laser scanning (TLS) point cloud registration for challenging natural environments. The new method improves efficiency and accuracy for large-scale 3D modeling and deformation monitoring in forests and mountains.

Keywords:
cosine similaritymulti-view convolutional neural networkspoint cloud registrationweak geometric features

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Area of Science:

  • Geomatics Engineering
  • Computer Vision
  • Geospatial Data Science

Background:

  • Outdoor terrestrial laser scanning (TLS) faces challenges in point cloud registration due to weak geometric features in natural landscapes like mountains and forests.
  • Existing algorithms struggle with alignment errors and low efficiency when dealing with large-scale outdoor data with irregular shapes and rough surfaces.

Purpose of the Study:

  • To develop a robust and efficient point cloud registration method for outdoor TLS data, particularly in environments with weak geometric features.
  • To address the limitations of current registration algorithms in terms of accuracy and speed for large-scale outdoor scene surveying.

Main Methods:

  • Utilized the NARF + SIFT algorithm for enhanced key point extraction.
  • Applied multi-view convolutional neural networks (MVCNN) for point cloud registration.
  • Employed GPU acceleration for matrix calculations to improve processing speed.

Main Results:

  • Demonstrated significant improvements in registration efficiency for outdoor TLS point clouds.
  • Ensured high registration accuracy even with data characterized by weak geometric features.
  • Successfully addressed the challenges posed by large scanning ranges and enormous data volumes.

Conclusions:

  • The proposed NARF + SIFT and MVCNN-based method offers a superior solution for outdoor TLS point cloud registration in complex natural environments.
  • This advancement enhances the feasibility of accurate 3D modeling and deformation monitoring in challenging terrains.
  • The method provides a more efficient and reliable approach to geospatial data acquisition and processing.