计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (15): 11-23.DOI: 10.3778/j.issn.1002-8331.2402-0008

• 热点与综述 • 上一篇    下一篇

面向施工机器人定位的多模态数据融合方法研究综述

李佳益,马智亮,陈礼杰,季鑫霖   

  1. 1.清华大学 土木工程系,北京 100084
    2.香港大学 土木工程系,香港 999077
  • 出版日期:2024-08-01 发布日期:2024-07-30

Comprehensive Review of Multimodal Data Fusion Methods for Construction Robot Localization

LI Jiayi, MA Zhiliang, CHENG Lijie, JI Xinlin   

  1. 1.School of Civil Engineering, Tsinghua University, Beijing 100084, China
    2.Department of Civil Engineering, The University of Hong Kong, Hong Kong 999077, China
  • Online:2024-08-01 Published:2024-07-30

摘要: 施工机器人的定位数据源种类繁多,融合多模态数据不仅有助于提升建筑项目中施工机器人的定位性能,同时也方便施工机器人的协同作业。数据融合方法旨在通过不同数据源的优势互补,改进数据采集及处理方法等,实现施工机器人的定位和数据共享,支持施工机器人定位精度、实时性或鲁棒性等的提高,从而提高整体建筑施工效率和项目管理水平。已有不少针对特定场景探索施工机器人定位的数据融合方法相关研究成果,但尚无针对施工机器人定位的数据融合方法相关研究综述。经系统的检索,首先,按照是否与先验数据融合,将其分为先验数据与传感器实时数据融合和多种传感器数据融合两类进行分析;然后,对数据融合方法进行对比分析;最后,总结和展望了施工机器人多模态数据融合方法的未来研究方向。从研究结果分析,现阶段已有的研究成果中,施工机器人定位的数据源选择差异性较大,定位效果差异也很大。该综述可为相关领域的进一步研究提供参考。

关键词: 施工机器人, 定位, 数据融合方法, 多模态数据

Abstract: The types of localization data sources for construction robots are diverse, and the fusion of multimodal data not only helps to improve the localization performance of construction robots in building projects but also facilitates their collaborative operations. Data fusion methods aim to enhance construction robot localization and data sharing by leveraging the advantages of different data sources, improving data collection and processing methods, this supports the improvement of construction robot localization accuracy, real-time performance, and robustness, ultimately enhancing overall construction efficiency and project management. While there are existing research outcomes on specific scenarios exploring data fusion methods for construction robot positioning, there is currently no comprehensive review article on this topic. Through systematic retrieval, this paper first categorizes the analysis into two types based on whether it integrates with prior data:fusion of prior data and real-time sensor data fusion, and fusion of data from multiple sensors. Subsequently, a comparative analysis of data fusion methods is conducted. Finally, the paper summarizes and anticipates the future research directions of multimodal data fusion methods for construction robots. The analysis of current research results reveals significant variations in the choice and effectiveness of positioning data sources for construction robots. This review can serve as a reference for further research in related fields.

Key words: construction robot, localization, data fusion method, multimodal data