基于改进的Cartographer框架,设计和实验SLAM系统,用于温室中低密度的树冠环境
Haoran Tan1,2, Xueguan Zhao2,3,4, Changyuan Zhai2,3
1College of Engineering, China Agricultural University, Beijing, China.
Frontiers in plant science
|February 16, 2024
概括
本研究引入了自适应过点云投影 (AF-PCP) SLAM算法,以改善温室中的农业机器人的映射和本地化. 新方法提高了准确性和绘制面积,即使在具有挑战性的低密度作物树冠上.
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:
- 同时定位和映射 (SLAM) 算法在低密度温室天窗中难以准确.
- 对于农业机器人来说,强大的导航对于高效的作物管理至关重要.
研究的目的:
- 开发和评估一种新的SLAM方法,用于精确地绘制和定位温室环境中的稀疏植被.
- 为了提高农业机器人的性能,在作物树冠内自主运行.
主要方法:
- 开发了一种基于LiDAR的多线SLAM方法,集成空间下采样和自适应过点云投影 (AF-PCP) 算法.
- 该方法利用车轮测距和16行LiDAR数据,具有适应性垂直投影,用于地图构建和姿势估计.
- 实验是在悬浮的番茄植物上进行的,其叶面积密度 (LAD) 不同.
主要成果:
- 与标准 Cartographer 算法相比,AF-PCP SLAM 算法显著增加了 155.7% 的作物行平均映射面积.
- 作物行长的平均误差和变化系数分别减少了77.9%和87.5%.
- 不同速度的平均相对定位错误很低 (0.0260.046 m),与轨道演算法相比减少了79.9%.
结论:
- 拟议的AF-PCP SLAM框架使农业机器人在低密度温室天窗中能够准确地绘制和定位.
- 该方法表现出强大的性能,并对精密农业的自主导航应用具有重大前景.
- 增强的绘图和本地化能力对于自动化农业系统的发展至关重要.
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