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Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds.

Hong Lang1,2, Yuan Peng2, Zheng Zou2

  • 1The Key Laboratory for Traffic and Transportation Security of Jiangsu Province, Huaiyin Institute of Technology, Huaian 223003, China.

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This study introduces a new method for extracting road curbs from unordered point clouds, improving perception for autonomous vehicles. The approach enhances curb detection accuracy in complex driving scenarios.

Keywords:
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Area of Science:

  • * Computer Vision
  • * Robotics
  • * Geospatial Data Analysis

Background:

  • * Road curb extraction is vital for autonomous vehicle navigation and road geometry analysis.
  • * Existing methods struggle with unordered point clouds and obstacle interference.
  • * Unordered point cloud data presents challenges due to its lack of inherent structure.

Purpose of the Study:

  • * To develop a robust curb extraction method for unordered point clouds.
  • * To overcome limitations of existing ordered point cloud-based approaches.
  • * To enhance the safety and reliability of autonomous vehicle perception systems.

Main Methods:

  • * Integration of multi-feature filtering: grid height difference, normal vectors, clustering, and alpha-shape algorithm.
  • * Application of M-Estimate Sample Consensus (MSAC) for multi-frame fitting to improve contour accuracy.
  • * Utilization of self-developed and Toronto datasets for comprehensive testing.

Main Results:

  • * Achieved high average precision (0.9365), recall (0.782), and F1 score (0.8523) across diverse scenarios.
  • * Demonstrated accurate and comprehensive curb point extraction in complex environments.
  • * Validated robustness against various conditions including intersections, straight roads, and curved roads.

Conclusions:

  • * The proposed multi-feature filtering method effectively extracts curbs from unordered point clouds.
  • * The approach enhances autonomous vehicle perception by providing accurate road geometry information.
  • * This method offers a robust solution for real-world curb detection challenges.