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Automated Algorithm for Removing Clutter Objects in Mms Point Cloud for 3D Road Mapping.

Jisang Lee1, Suhong Yoo1, Seunghwan Hong2

  • 1School of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.

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Summary
This summary is machine-generated.

This study presents a method for automatically removing clutter objects from high-definition (HD) maps created by mobile mapping systems (MMS). The technique achieves 91% accuracy, crucial for autonomous driving and road maintenance applications.

Keywords:
HD mapclutter objectsinstance segmentationmobile mapping systempoint cloud removal

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Area of Science:

  • Geomatics Engineering
  • Computer Vision
  • Robotics

Background:

  • High-definition (HD) maps are vital for autonomous driving and road maintenance.
  • Mobile mapping systems (MMS) are used to create these maps.
  • Acquiring unstructured data, termed clutter objects, during MMS operation is challenging.

Purpose of the Study:

  • To develop an automated method for removing clutter objects from MMS-generated HD maps.
  • To improve the quality and usability of HD map data.

Main Methods:

  • Utilized characteristics of mobile mapping systems (MMS).
  • Employed image segmentation techniques for object identification and removal.
  • Defined and addressed the issue of clutter objects in point cloud data.

Main Results:

  • Successfully removed clutter objects from 10 KITTI datasets.
  • Achieved an average overall accuracy of 91% for clutter object removal.
  • Maintained 0% error of commission (0.448%) for the complete point cloud map.

Conclusions:

  • The proposed method effectively removes clutter objects from HD maps.
  • This technique enhances the reliability of HD maps for autonomous driving and infrastructure management.
  • The high accuracy and low error rate demonstrate the method's practical applicability.