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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.
Sensors (Basel, Switzerland)
|May 31, 2018
Summary
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.
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.
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