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Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping.

Xuyou Li1, Shitong Du1, Guangchun Li1

  • 1College of Automation, Harbin Engineering University, Harbin 150001, China.

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Summary

This study introduces a segment-based scan-matching framework for autonomous systems, improving six degree-of-freedom pose estimation and mapping. The method enhances accuracy and reduces runtime compared to standard Iterative Closest Point (ICP) algorithms.

Keywords:
6D SLAMICPclosed loopsdynamic environmentsground pointsegmentation

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Iterative Closest Point (ICP) is crucial for LiDAR scan-matching in robotics.
  • Standard ICP struggles with accuracy in dynamic environments and real-time processing of large datasets.
  • Ground points from LiDAR scans often introduce noise and computational burden.

Purpose of the Study:

  • To develop a robust and efficient scan-matching framework for six degree-of-freedom (6-DoF) pose estimation and mapping.
  • To improve the accuracy and reduce the runtime of point-cloud registration for autonomous systems.
  • To address the limitations of standard ICP in challenging environments and with large-scale data.

Main Methods:

  • A segment-based scan-matching framework utilizing image-based ground-point extraction.
  • Filtering of noise and ground points to optimize point cloud data.
  • Application of point-to-point ICP for 6-DoF transformation estimation between scans.
  • Integration of a 6D graph-optimization algorithm for global relaxation (6D SLAM) upon loop closure detection.

Main Results:

  • The proposed method demonstrates reduced runtime compared to standard ICP and its variants.
  • Higher pose estimation accuracy is achieved in experiments using the KITTI dataset.
  • Effective filtering of ground points and segmentation improves processing efficiency.

Conclusions:

  • The segment-based scan-matching framework offers a more efficient and accurate solution for 6-DoF pose estimation and mapping.
  • The approach effectively handles noisy data and large point clouds, outperforming traditional ICP.
  • This work contributes to advancing the capabilities of autonomous mobile systems in complex environments.