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Adaptive Obstacle Detection for Mobile Robots in Urban Environments Using Downward-Looking 2D LiDAR.

Cong Pang1, Xunyu Zhong2, Huosheng Hu3

  • 1Department of Automation, Xiamen University, Xiamen 361102, China. 23220161151527@stu.xmu.edu.cn.

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This study introduces an adaptive obstacle detection method for outdoor mobile robots using LiDAR. The approach accurately identifies road obstacles, enhancing collision-free motion planning in urban environments.

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Effective environment perception is crucial for collision-free motion planning in outdoor mobile robots.
  • Existing methods may struggle with varying road conditions and obstacle heights.

Purpose of the Study:

  • To develop an adaptive obstacle detection method for outdoor mobile robots.
  • To utilize a single downward-looking LiDAR sensor for robust environment perception.

Main Methods:

  • Extracting line segments from raw LiDAR data.
  • Estimating road surface height and vector.
  • Classifying segments as ground or obstacles based on height and vector deviation.

Main Results:

  • The method accurately detects obstacles on roads in various scenarios.
  • It effectively handles obstacles of different heights in urban road environments.

Conclusions:

  • The proposed adaptive obstacle detection method enhances the safety and reliability of outdoor mobile robots.
  • This approach contributes to improved navigation and motion planning in complex urban settings.