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基于LiDAR和相机融合技术的玉米叶和茎识别和测距技术的研究

Xueting Hu¹, Xiao Zhang¹, Xi Chen¹

¹Mechanical Engineering Training Centre, College of Engineering, China Agricultural University, Beijing 100083, China.

Sensors (Basel, Switzerland)

|August 29, 2024

概括

这项研究引入了一种新的多传感器融合算法,用于精确识别和测距玉米叶和茎. 这项技术增强了农业机器人的导航和在玉米田中的数据收集.

科学领域:

农业机器人农业机器人
计算机视觉计算机视觉
传感器融合式传感器

背景情况:

高精度导航对于玉米田中的农业机器人至关重要.
精确的识别和范围的玉米叶和茎是有效的机器人导航的先决条件.
由于叶子形态和现场照明的变化,现有的方法面临挑战.

研究的目的:

开发一个强大的多传感器融合算法,用于玉米叶和茎的识别和测距.
提高农业机器人的识别和距离测量的准确性和完整性.
确保在玉米田中稳定高效的机器人操作.

主要方法:

使用YOLOv8进行初始玉米叶和茎检测.
实现了一个同等距离的扩展多边形算法,用于后处理叶子识别,提高完整性.
合并的LiDAR和深度摄像头点云使用IMU数据进行准确的距离测量,平均误差为2.9厘米.
采用FILL-DBSCAN算法用于茎点云处理和集群.

主要成果:

在后处理后,实现了平均86.4%的叶子识别完整性.
证明了高精度的测距精度,玉米叶的平均误差为2.9厘米.
成功识别并测量到各个玉米茎的距离.

结论:

拟议的多传感器融合算法有效地整合了识别和距离精度.
这种方法满足了机器人导航和玉米田的表型测量需求.
在动态的玉米田环境中确保农业机器人的可靠和高效的性能.

关键词:

玉米的叶子和茎.多传感器融合融合技术识别和距离测量的测量.

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