Abstract: [Purposes]To reduce the influence of coal shed environment and mechanical equipment on 3D modeling of coal yard and improve the accuracy of point cloud data of coal yard.This paper proposes a combined denoising algorithm for coal yard point clouds based on regional growth algorithm and multi-scale elevation difference.[Methods]The algorithm mainly includes two steps: coarse sampling and fine screening.Firstly,based on the similarity of geometric features,the regional growth algorithm is used to find the abnormal points that are different from the main structure of the coal yard point cloud,and the rough extraction of the noise point set is completed.Secondly,according to the uniqueness of the neighborhood spatial distribution of abnormal points,the multi-scale elevation difference algorithm is used to eliminate non noise points,and complete the fine screening of noise point set.[Findings]The experimental results show that the similarity error of the proposed algorithm based on 1.23% noise removal rate is 0.162 343 and 0.038 870 6 respectively.[Conclusions]It is about 6.54% and 46.8% higher than the traditional area growth algorithm.The proposed algorithm can effectively remove noise points while preserving the complete subject information of the coal yard point cloud,providing data support for the subsequent volume measurement of the coal yard.

Key words: coal yard point cloud, regional growth, multi-scale elevation difference, denoising