# A Scraper Conveyor Coal Flow Monitoring Method Based on Speckle Structured Light Data
## 4 Methodology  
### PDS-Algorithm  
Before using the point cloud data to calculate the coal volume, it is necessary to simplify the collected point cloud data. At present, scholars have proposed different point cloud simplification algorithms in order to achieve different simplification effects. These algorithms are roughly divided into three categories. The first category is to simplify scattered point clouds according to mathematical ideas, such as the random sampling method and its improvement method [24,25]. This kind of method can directly simplify the point cloud data as a whole, and the efficiency is relatively high. The second category is to divide the original point cloud data into different grids [26,27]. By simplifying the point cloud data in each grid, such as the uniform grid method, this method requires grid construction before data simplification. The third category is to simplify the point cloud based on the differential geometric information of the point and its neighboring points, such as the curvature method and the minimum distance method [28].  
By analyzing the point cloud data collected by speckle structured light acquisition equipment, we found that this kind of data is very different from the data scanned by devices such as lidar. This difference is embodied in two points:  
1. The area of point cloud data collected by speckle equipment is highly controllable. This is also one of the reasons for choosing the speckle equipment to collect the point cloud data of the scraper conveyor. We only care about the point cloud data of the coal body in the area of the scraper conveyor, not the point cloud data of the environment outside the scraper conveyor.
2. The accuracy of point cloud data collected by speckle structured light equipment is limited. The collected point cloud data can only reflect the overall outline of the coal and there are some voids, rather than the uniformity and density of the coal collected by lidar equipment. Because the focus of this paper is the coal volume on the scraper conveyor, the point cloud data collected by the speckle structured light can basically meet the accuracy requirements for calculating the coal volume in the area.  
Compared with lidar, the images collected by speckle structured light will have an uneven density of point cloud data due to the difference in the angle of view collected by the binocular camera. As we all know, the denser the distribution of collected point cloud data, the higher the accuracy of the collection area. In view of the uneven distribution and limited accuracy of speckle structured light data, this paper proposes a point cloud data simplification algorithm based on plane density (PDS-Algorithm). As the height coordinates of the point cloud data are the most concerned in this paper, not the three-dimensional features, this paper selects the uniform grid method that can make full use of the height coordinates of the point cloud data as the improved basic algorithm. As shown in Figure 4, the flow chart of the PDS-Algorithm. In the following sections, the Coordinate  
Figure 3: Technical route of coal flow monitoring research on scraper conveyor.  
translation, Density calculation, point cloud meshing and point cloud data simplification in the flowchart will be introduced one after another.