面向施工机器人定位的多模态数据融合方法研究综述OA北大核心CSTPCD

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 col-laborative 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 improve-ment of construction robot localization accuracy,real-time performance,and robustness,ultimately enhancing overall con-struction 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.