摘要
在智能制造与工业4.0的推动下,工业测量技术面临着提升精度、适应复杂环境及多样化工件的严峻挑战,传统单一传感器难以满足需求,多传感器数据融合技术通过集成异构传感器的互补优势,能够显著提升测量系统的可靠性、精度及环境适应性。本文通过系统梳理多传感器融合工业测量技术的研究现状,重点分析了高精度静态测量、动态移动测量及跨模态混合测量3种典型系统的配置及其协同测量方法,归纳了数据级、特征级及决策级融合算法的关键技术与发展趋势,分析了多传感器融合面临的时空基准标定、环境噪声抑制、实时计算优化及跨模态语义对齐等关键技术挑战。未来,多传感器融合技术将向更加智能化、精准化及自适应的方向演进。
Driven by intelligent manufacturing and Industry 4.0,industrial measurement technology is facing severe challenges in improving accuracy,adapting to complex environments,and diversifying workpieces.Traditional single sensors are difficult to meet the needs.Multi-sensor data fusion technology can significantly improve the reliability,accuracy,and environmental adaptability of measurement systems by integrating the complementary advantages of heterogeneous sensors.By systematically reviewing the research status of multi-sensor fusion industrial measurement technology,this paper focuses on analyzing the configuration and collaborative measurement methods of three typical systems:high-precision static measurement,dynamic mobile measurement,and cross modal hybrid measurement.The key technologies and development trends of data level,feature level,and decision level fusion algorithms are summarized,and the key technical challenges faced by multi-sensor fusion,such as spatiotemporal benchmark calibration,environmental noise suppression,real-time computation optimization,and cross modal semantic alignment,are analyzed.In the future,multi-sensor fusion technology will evolve towards greater intelligence,precision,and adaptability.
作者
郭迎钢
石风淼
徐嘉泽
GUO Yinggang;SHI Fengmiao;XU Jiaze(Northwest Institute of Nuclear Technology,Xi'an 710024,China)
出处
《测绘通报》
北大核心
2025年第S1期44-49,共6页
Bulletin of Surveying and Mapping
关键词
多传感器融合
工业测量
数据融合算法
全站仪
激光跟踪仪
智能制造
multi-sensor fusion
industrial measurement
data fusion algorithm
total station
laser tracker
intelligent manufacturing
作者简介
郭迎钢(1992-),男,博士,高级工程师,主要研究方向为精密工程测量。E-mail:fariel_gyg@163.com
