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

Abstract

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.

Keywords

• laser scanning
• bulk grain pile
• grain surface variation
• image recognition
• point cloud data