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Segment-Based SLAM Registration Optimization Algorithm Combining NDT and PL-ICP.

Yi Zhang1, Xiao Wang1, Xiuqin Lyu2

  • 1School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China.

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|December 11, 2025
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Summary
This summary is machine-generated.

Solid-state LiDAR SLAM struggles with large-scale error accumulation. A new segment-based Normal Distributions Transform (NDT) and Point-to-Line Iterative Closest Point (PL-ICP) algorithm significantly enhances mobile mapping accuracy.

Keywords:
SLAMregistrationsolid-state LiDAR

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Area of Science:

  • Robotics and Autonomous Systems
  • Geospatial Technology
  • Computer Vision

Background:

  • Solid-state LiDAR offers cost-effective measurement solutions.
  • Simultaneous Localization and Mapping (SLAM) algorithms like LOAM-Livox face registration errors in large-scale environments with solid-state LiDAR.
  • Error accumulation limits the applicability of current SLAM methods for solid-state LiDAR in mobile mapping.

Purpose of the Study:

  • To develop an optimized SLAM registration algorithm for solid-state LiDAR to mitigate error accumulation.
  • To enhance the accuracy and reliability of mobile mapping using solid-state LiDAR in large-scale environments.

Main Methods:

  • A novel segment-based SLAM registration optimization algorithm combining Normal Distributions Transform (NDT) and Point-to-Line Iterative Closest Point (PL-ICP).
  • Data processing is divided into segments, with independent SLAM execution per segment.
  • Overlapping areas between adjacent segments are registered to minimize accumulated errors.

Main Results:

  • The proposed NDT + PL-ICP optimization algorithm significantly improves mobile mapping accuracy with solid-state LiDAR.
  • Experimental results on public and self-collected datasets validate the algorithm's effectiveness.
  • The method successfully resolves the error accumulation issue inherent in SLAM for solid-state LiDAR.

Conclusions:

  • The segment-based NDT + PL-ICP SLAM optimization algorithm is effective and practical for real-world mobile mapping applications using solid-state LiDAR.
  • This approach enhances the precision of large-scale environmental mapping.
  • The study confirms the algorithm's capability to overcome limitations of existing SLAM techniques.