基于激光雷达的散粮堆粮面异动识别算法研究与验证

Research and Validation of a Lidar-based Algorithm for Recognizing Grain Surface Variations in Bulk Grain Piles

粮面异动监测是国家储备粮日常库存检查的重要内容,是粮食仓储监管的一项新需求。为了解决传统粮面异动监测技术难题,本文提出了一种基于激光雷达的粮面异动监测方法。通过搭建一个能够模拟真实粮堆状态的足尺实验平台,利用高精度激光三维测量装置,设计了基于激光扫描点云数据和出入仓作业信息的散粮堆粮面异动识别算法,并通过实验平台进行了算法检验与验证。结果表明:该方法可直接获取高精度的粮堆表面坐标信息,克服了图像识别技术数据精度不足的问题;构建的基于粮堆形貌坐标信息和出入库作业状态数据的粮面异动判据算法,能够实现可靠的定量计算;将所提出的方法应用于实际粮仓场景,验证了该方法的可行性和有效性,能够满足粮食库存动态监管需求,为粮面异动在线监测及预警提供了新技术。

Grain surface movement monitoring is an important part of the daily inventory inspection of national grain reserves,and is a new requirement for grain storage supervision.In order to solve the technical problems of the traditional grain surface movement monitoring,this paper proposed a laser radar-based grain surface movement monitoring method.A full-size experimental platform that can simulate the real grain pile state was established,and a high-precision laser three-dimensional measurement device was utilized to design an algorithm to identify the abnormal movement of the grain surface of the bulk grain pile based on the laser scanning point cloud data and the information of entering and exiting the warehouse operation.The algorithm was examined and verified through the experimental platform.The results showed that:the method can directly obtain high-precision coordinate information of the grain pile surface,which overcome the problem of insufficient data accuracy of image recognition technology.The constructed algorithm of grain surface motion discrimination based on the coordinate information of the grain pile shape and the data of entry and exit operation status was capable of realizing reliable quantitative computatio.The proposed method was applied to the actual grain warehouse scenario,which verified the feasibility and validity of the method,and it was able to satisfy the dynamic grain inventory supervision needs.The proposed method was applied to the actual grain silo scenario to verify the feasibility and effectiveness of the method,which can meet the demand of grain stock dynamics supervision and provide a new technology for online monitoring and early warning of grain surface variation.