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Real-Time HD Map Change Detection for Crowdsourcing Update Based on Mid-to-High-End Sensors.

Pan Zhang1, Mingming Zhang2, Jingnan Liu1

  • 1School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China.

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Summary
This summary is machine-generated.

This study presents a real-time method for updating high-definition (HD) maps using vehicle sensors. The approach effectively detects changes and crowdsources updates for autonomous driving applications.

Keywords:
HD mapautonomous drivingcrowdsourcing updatesemantic segmentationvisual SLAM

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Area of Science:

  • Computer Vision
  • Robotics
  • Geographic Information Systems (GIS)

Background:

  • Maintaining up-to-date High-Definition (HD) maps is crucial for autonomous driving but presents significant challenges.
  • Increasingly sophisticated sensors and computing power in vehicles offer opportunities for real-time map updates.

Purpose of the Study:

  • To propose a novel, real-time method for detecting changes in HD maps suitable for crowdsourcing.
  • To leverage onboard vehicle sensors for efficient and accurate HD map maintenance.

Main Methods:

  • Utilized a high-end Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) for precise vehicle positioning (average 20 cm accuracy).
  • Employed an improved BiSeNet for real-time semantic segmentation, achieving 83.9% Intersection over Union (IOU) at 31 FPS.
  • Integrated visual Simultaneous Localization and Mapping (SLAM) with pixel type information to generate semantic point clouds of road features, followed by vectorization, denoising, clustering, and matching against existing HD maps.

Main Results:

  • The semantic segmentation network demonstrated high performance on the Nvidia Pegasus platform.
  • The integrated approach successfully identified unchanged map elements and detected new features.
  • Experimental validation in Beijing confirmed the method's effectiveness for crowdsourced HD map updates.

Conclusions:

  • The proposed method provides an effective solution for the continuous maintenance and real-time update of HD maps.
  • This approach supports the advancement of autonomous driving systems through reliable and dynamic map data.