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利达尔IMU融合导航系统用于智能工厂中的AGV
Haichao Li1, XianZhou Wu1, Liang Wang1
1Hangzhou Aolida Elevator Co., Ltd., Hangzhou, P. R. China.
PloS one
|October 27, 2025
概括
本研究介绍了用于自动引导车辆 (AGV) 的选惯性数据融合方法 (SIDFM). 通过智能融合LiDAR和IMU数据,SIDFM提高了AGV导航准确度,减少了智能工厂中的错误.
科学领域:
- 机器人和自动化机器人与自动化
- 传感器融合式传感器
- 导航系统 导航系统
背景情况:
- 自动引导车辆 (AGV) 对于智能工厂的物料运输至关重要.
- 精确的AGV导航面临的挑战是由于高维LiDAR数据和间歇性的惯性测量单元 (IMU) 信号,导致漂移.
- 现有的LiDAR-IMU融合方法与数据不一致性作斗争.
研究的目的:
- 通过解决 LiDAR-IMU 数据融合挑战,开发一个强大的 AGV 导航的新框架.
- 提高AGV在动态工业环境中的精度,稳定性和安全性.
- 为了减少AGV的导航和定位错误.
主要方法:
- 提出了用于AGV导航的选惯性数据融合方法 (SIDFM).
- 使用最小差值函数系统选LiDAR数据.
- 通过线性回归学习融合选的LiDAR数据与IMU间隔.
主要成果:
- 在低加速时,SIDFM减少了12.09%的导航错误,在高加速时减少了11.43%.
- 与基线方法相比,定位错误显著减少.
- 证明了AGV的增强稳定性和精度.
结论:
- SIDFM提供了一个有效和实用的解决方案,用于强大的AGV导航.
- 该方法在动态制造环境中提高了AGV的性能.
- 在智能工厂,仓库和自主移动系统中,SIDFM具有潜在的应用.
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