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LESA-Net:在复杂的农林环境中,多种类型的道路点云的语义细分

Yijian Duan¹, Danfeng Wu^2,3, Liwen Meng¹

¹College of Mechanical Engineering, Guangxi University, Naning, 530004, Guangxi, China.

|September 19, 2024

概括

本研究介绍了一种新的点云语义细分网络,用于农业机器人. 该框架使用双距离自我注意力来准确地分割大型农林环境中的道路表面.

科学领域:

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
机器学习机器学习

背景情况:

点云语义细分对于农业林业中的农业机器人至关重要.
大规模的点云数据在针对细分的特征学习方面带来了挑战.
准确的环境理解对于机器人导航和操作至关重要.

研究的目的:

为大规模农林环境开发一个精确的点云语义细分网络.
为了应对来自大规模点云数据集的特征学习的挑战.
提高农业机器人的理解周围环境的能力.

主要方法:

提出了一个点云语义细分网络框架,利用双距离自我注意力.
引入了本地特征增强模块,扩展受体场,增强特征通用性.
开发了一个双距离注意力聚合 (DDAPS) 块,用于聚合歧视性的当地社区特征.

主要成果:

拟议的网络实现了道路表面点云的精确语义细分.
在SemanticKITTI和RELLIS-3D数据集上的实验结果显示出卓越的性能.
该算法在大型农林环境中表现优于现有的方法.

结论:

双距离自我注意框架有效地增强了点云语义细分.
提出的方法为在复杂环境中运行的农业机器人提供了强大的解决方案.
这项工作有助于提高农业自主系统的能力.

关键词:

农业机器人农业机器人深度学习是一种深度学习.一个点云点云.道路表面细分路面的划分.

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