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A real-time road detection method based on reorganized lidar data.

Fenglei Xu1, Longtao Chen1, Jing Lou1

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This study introduces a novel method for real-time road detection using 3D lidar data. The approach enhances spatial relationships and utilizes new features for accurate road identification in automated driving.

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Geospatial Data Analysis

Background:

  • Road detection is crucial for automated driving systems.
  • 3D lidar data offers rich environmental information for this task.
  • Existing methods may struggle with unstructured environments.

Purpose of the Study:

  • To develop a novel, efficient, and accurate road detection method using 3D lidar data.
  • To introduce new features derived from lidar point clouds for improved road identification.
  • To enhance the spatial relationship representation of lidar data.

Main Methods:

  • Reorganizing 3D lidar data into a structured format.
  • Introducing 'difference-between-lines' and 'ScanID density' features.
  • Constructing Bird's Eye View (BEV) consistency and obstacle maps.
  • Fusing maps and applying refinement for final road extraction.

Main Results:

  • Achieved top performance on the KITTI-Road benchmark for lidar-based road detection.
  • Demonstrated effectiveness in unstructured road environments, including rural areas.
  • The proposed features significantly contribute to accurate road segmentation.

Conclusions:

  • The proposed method offers a robust solution for real-time road detection using 3D lidar.
  • The novel features and data reorganization enhance spatial understanding for autonomous navigation.
  • This approach shows promise for improving safety and reliability in diverse driving conditions.