走向机器人修剪:在使用RGB-D图像重建的树木上进行树枝修剪的自动注释和预测
Jana Dukić1, Petra Pejić1, Ivan Vidović1
1Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osijek, Croatia.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种自动化管道,用于预测需要修剪的果树树枝. 它使用3D重建和机器学习来精确地在果园中进行机器人修剪.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 精准农业需要高效和自动化的修剪系统.
- 目前的机器人修剪缺乏准确的实时分支识别.
研究的目的:
- 开发一个全面的管道,用于果树的自动修剪预测.
- 通过先进的3D建模和机器学习,实现精确高效的机器人修剪.
主要方法:
- 多视图RGB-D图像捕获用于使用TEASER++对梨树进行3D重建.
- 通过比较修剪前和修剪后的3D模型进行自动分支标签.
- 训练一个PointNet++神经网络,在点云上进行直接的剪裁预测.
主要成果:
- 成功的3D重建和自动标记可剪切的树枝.
- 一个训练有素的神经网络,能够直接在点云上实时修剪预测.
- 有希望的性能指标和自动化管道的视觉验证.
结论:
- 开发的管道为自主果园管理提供了坚实的基础.
- 这种方法显著提高了机器人修剪系统的精度,速度和实用性.
- 未来的工作将集中在完善准确性和扩大适用于各种果园环境的适用性.
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