Fast Estimation of Loader’s Shovel Load Volume by 3D Reconstruction of Material Piles  

作　　者：Binyun Wu Shaojie Wang Haojing Lin Shijiang Li Liang Hou 

机构地区：[1]Pen-Tung Sah Institute of Micro-Nano Science and Technology,Xiamen University,Xiamen 361102,China [2]Shenzhen Research Institute of Xiamen University,Shenzhen 518057,China

出　　处：《Chinese Journal of Mechanical Engineering》2023年第5期187-205,共19页中国机械工程学报（英文版）

基　　金：Supported by National Key R&D Program of China(Grant Nos.2020YFB1709901 and 2020YFB1709904);National Natural Science Foundation of China(Grant Nos.51975495 and 51905460);Guangdong Provincial Basic and Applied Basic Research Foundation(Grant No.2021A1515012286);Guiding Funds of Central Government for Supporting the Development of the Local Science and Technology(Grant No.2022L3049).

摘　　要：Fast and accurate measurement of the volume of earthmoving materials is of great signifcance for the real-time evaluation of loader operation efciency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fll factor estimation, and it has signifcant theoretical research and engineering application value.

关 键 词：LOADER Volume estimation Binocular stereo vision 3D terrain reconstruction Point cloud registration and segmentation 

分 类 号：U469.7[机械工程—车辆工程] U464[交通运输工程—载运工具运用工程]