基于LiDAR的负障碍探测用于果园中的无人地面车辆
Peng Xie1, Hongcheng Wang1, Yexian Huang1
1School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究引入了一种基于LiDAR的方法,用于检测果园中的沟等危险的地面障碍物. 通过倾斜传感器,可以提高机器人的安全性,从而实现更可靠的自主操作.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 农业工程 农业工程
- 计算机视觉 计算机视觉
背景情况:
- 果园中的自主机器人面临来自沟和坑等负面障碍的安全风险.
- 现有的检测方法可能难以应对果园地形不均的特殊挑战.
研究的目的:
- 开发和验证一种使用LiDAR检测果园环境中的负面障碍的新方法.
- 提高在农业环境中运行的自主机器人的安全性和可靠性.
主要方法:
- 使用倾斜的光检测和射程 (LiDAR) 传感器 (40°倾斜) 来改进地面点云数据.
- 分析激光点云的几何特征,包括后墙高度,密度和点间距.
- 开发一个数学模型,集成多点云数据,以进行可靠的检测.
主要成果:
- 盲点从3米减少到0.21米,地面点云密度增加.
- 在实验试验中检测负面障碍的成功率达到了92.7%.
- 为负障碍物建立了大约8.0米的最大检测距离.
结论:
- 拟议的LiDAR倾斜安装方法有效地检测果园中的负面障碍物.
- 这项技术显著提高了机器人的安全性,并为果园自动化的进步做出了贡献.
- 该方法为减轻与农业机器人地面危险相关的风险提供了一个实际的解决方案.
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