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Robust point cloud registration based on semantic iterative closest point algorithm.

Shaoyi Du1, Tiancheng Shao1,2, Canhui Tang1

  • 1National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China.

Fundamental Research
|June 11, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a robust point cloud registration algorithm using semantic information and bidirectional search. The method enhances accuracy and resilience against noise and outliers in LiDAR data.

Area of Science:

  • Computer Vision
  • Robotics
  • Geospatial Analysis

Background:

  • Point cloud registration is crucial for 3D data processing but faces challenges with noisy LiDAR data and poor initial alignment.
  • Existing methods struggle with outliers and significant positional errors, limiting their real-world applicability.

Purpose of the Study:

  • To develop a robust and accurate point cloud registration algorithm for LiDAR data.
  • To improve registration performance in the presence of noise, outliers, and poor initial positions.

Main Methods:

  • A semantic-based iterative closest point (ICP) algorithm incorporating bidirectional distance and correntropy.
  • Semantic-guided correspondence establishment to narrow search ranges and enhance accuracy.
  • Bidirectional semantic search point matching for improved error correction.
Keywords:
Bidirectional distanceIterative closest pointMaximum correntropy criterionPoint cloud registrationSemantic information

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  • Maximum correntropy criterion for noise and outlier suppression.

    • Main Results:

    • The proposed algorithm demonstrates superior accuracy and robustness compared to existing registration methods.
    • Semantic guidance effectively improves correspondence establishment and overall registration precision.
    • The use of correntropy significantly enhances resilience to noise and outliers in LiDAR point clouds.

    Conclusions:

    • The semantic-based ICP algorithm offers a robust solution for challenging LiDAR point cloud registration tasks.
    • The integration of semantic information and advanced outlier rejection techniques leads to significant performance gains.
    • This method advances the state-of-the-art in 3D point cloud registration for applications requiring high accuracy and reliability.