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乐高-LOAM-FN:一种改进的同时定位和绘图方法,在复杂的果园环境中融合了LeGO-LOAM,Faster_GICP和NDT

Jiamin Zhang¹, Sen Chen¹, Qiyuan Xue¹

¹School of Software Technology, Shanxi Agricultural University, Jinzhong 030801, China.

Sensors (Basel, Switzerland)

|January 23, 2024

概括

这项研究引入了一种新的循环回归注册算法,用于果园中的机器人,显著减少绘图中的累积错误. 新方法提高了复杂农业环境中的机器人导航精度.

科学领域:

机器人技术机器人技术机器人技术
计算机视觉计算机视觉
农业工程农业工程

背景情况:

复杂果园中的机器人绘图面临着由于大场景,特征相似性和运动不稳定的累积错误.
现有的算法在动态和视觉上相似的农业环境中难以准确.

研究的目的:

开发一个强大的循环回归注册算法,用于精确地绘制果园中的机器人映射.
在具有挑战性的农业环境中减轻 LiDAR 测距和绘图 (LOAM) 系统的累积错误.

主要方法:

快速通用代最接近点 (Faster_GICP) 和正常分布变换 (NDT) 的融合用于循环回归注册.
使用K维树 (KD-Tree) 来动态移除障碍点云.
实现了两步点过器和黑森矩阵优化,以提高注册精度.

主要成果:

拟议的算法与轻量级和地面优化的LiDAR口径测量和绘图 (LeGO-LOAM) 相比,显示出更高的性能.
在轨道准确性方面,实现了显著较低的根平均平方误差 (0.45 m) 和标准偏差 (0.26 m).
与LeGO-LOAM相比,在减少轨迹误差方面表现出67%和73%的改善.

结论:

开发的回路注册算法有效地减少了果园映射中的累积错误.
为推进智能农业运作提供关键的技术支持.
在非结构化环境中提供更可靠的解决方案,用于机器人本地化和绘制地图.

关键词:

李达尔 (LiDAR) 是一种激光雷达.斯拉姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯循环回归检测检测回路检测检测移动机器人移动机器人果园环境果园环境果园环境点云对齐点云对齐情况

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