Laser-based material flow detection method for belt conveyors using Akima interpolation
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摘要:
针对现有基于激光雷达的带式输送机物料流量检测方法易受异常点云数据影响、难以准确描述物料表面状态的问题，提出一种基于Akima插值的带式输送机物料流量激光检测方法。通过激光雷达获取输送带点云轨迹，并进行直通滤波和离群点去噪处理；采用Akima插值法获取带式输送机上物料的截面积，结合输送带运行速度和激光雷达扫描频率，计算单个扫描周期内的物料体积；通过对任意时间段的测量数据进行积分，获得该时间段内的物料总体积。仿真结果表明，对激光雷达输出的点云进行离群点去噪处理，能够有效识别异常的点云数据并对其进行修正，修正后的计算结果更接近真实的物料截面积。分别采用扇形−三角形计算法和Akima插值法对不同体积和带速的情况进行对比实验，结果表明，扇形−三角形计算法的精度较低且不稳定，而Akima插值法的精度全部达90%以上，可靠性高，可以准确得到输送物料的瞬时流量和总流量。
Abstract:To address the issue that existing LiDAR-based material flow detection methods for belt conveyors are susceptible to abnormal point cloud data and struggle to accurately describe the surface state of materials, a laser-based material flow detection method for belt conveyors using Akima interpolation is proposed. The method involved acquiring point cloud trajectories of the conveyor belt using LiDAR, followed by pass-through filtering and outlier noise removal. The Akima interpolation method was then used to obtain the cross-sectional area of material on the belt. Combined with the conveyor's operating speed and LiDAR scanning frequency, the material volume within a single scan cycle was calculated. By integrating the measurement data over any given time period, the total material volume during that period could be obtained. Simulation results showed that denoising outlier points from the LiDAR output point cloud could effectively identify and correct abnormal data, resulting in calculated values that were closer to the actual material cross-sectional area. Comparative experiments using both the sector-triangle calculation method and the Akima interpolation method under varying volumes and belt speeds demonstrated that the sector-triangle method had lower and less stable accuracy, while the Akima interpolation method consistently achieved accuracy above 90%, offering high reliability and enabling accurate measurement of both instantaneous and total material flow.
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Keywords:
- belt conveyor /
- flow detection /
- LiDAR /
- Akima interpolation method /
- outlier denoising
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图 3 激光雷达获取的输送带点云轨迹
Figure 3. Point cloud trajectory of the conveyor belt obtained by LiDAR
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图 5 带式输送机输送物料总体积计算
Figure 5. Calculation of total volume of materials conveyed by belt conveyor
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图 6 带式输送机空载时的激光扫描截面
Figure 6. Laser scanning cross-section of belt conveyor under no-load condition
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图 7 插值前后输送带点云分布
Figure 7. Point cloud distribution of the conveyor belt before and after interpolation
表 1 扇形−三角形计算法实验数据
Table 1 Experimental data of sector-triangle calculation method
实际体积/dm3 带速/（m·min−1） 实验1 实验2 实验3 实验4 实验5 检测值/dm3 精度/% 检测值/dm3 精度/% 检测值/dm3 精度/% 检测值/dm3 精度/% 检测值/dm3 精度/% 1.8 5 2.011 88.26 1.731 96.18 1.591 88.41 1.689 93.83 1.756 97.61 10 1.945 91.97 1.924 93.11 2.053 85.96 1.923 93.17 1.806 99.67 15 1.812 99.34 1.801 99.92 2.002 88.78 1.884 95.32 1.869 96.17 20 2.202 77.63 2.048 86.23 2.081 84.38 2.043 86.52 2.096 83.54 2.4 5 2.737 85.96 2.592 92.0 2.581 92.44 2.446 98.08 2.577 92.61 10 2.776 84.35 2.589 92.13 2.664 89.00 2.859 80.87 2.741 85.79 15 2.799 83.37 2.888 79.68 2.697 87.61 2.883 79.89 2.639 90.05 20 2.667 88.87 2.785 83.97 2.708 87.16 2.710 87.07 2.810 82.92 3.0 5 3.165 94.49 3.175 94.18 3.269 91.04 3.391 86.98 3.233 92.22 10 3.212 92.94 3.151 94.97 3.275 90.83 3.318 89.41 3.420 86.01 15 3.300 89.98 3.352 88.28 3.381 87.28 3.474 84.18 3.352 88.28 20 3.230 92.34 3.402 86.59 3.470 84.34 3.175 94.16 3.501 83.31 
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表 2 Akima插值法实验数据
Table 2 Experimental data of Akima interpolation method
实际体积/dm3 带速/（m·min−1） 实验1 实验2 实验3 实验4 实验5 检测值/dm3 精度/% 检测值/dm3 精度/% 检测值/dm3 精度/% 检测值/dm3 精度/% 检测值/dm3 精度/% 1.8 5 1.770 98.36 1.739 96.63 1.671 92.81 1.687 93.72 1.867 96.29 10 1.894 94.78 1.819 98.94 1.853 97.04 1.621 90.03 1.745 96.97 15 1.734 96.36 1.954 91.44 1.695 94.17 1.795 99.74 1.733 96.27 20 1.728 96.01 1.583 87.93 1.622 90.11 1.740 96.67 1.757 97.61 2.4 5 2.637 90.13 2.407 99.71 2.547 93.86 2.206 91.91 2.335 97.29 10 2.197 91.55 2.332 97.18 2.433 98.61 2.383 99.31 2.563 93.2 15 2.399 99.94 2.581 92.45 2.523 94.87 2.616 91.02 2.326 96.93 20 2.209 92.06 2.302 95.90 2.241 93.37 2.546 93.92 2.624 90.67 3.0 5 3.002 99.94 2.965 98.85 3.071 97.63 3.264 91.22 3.101 96.65 10 2.986 99.52 2.875 95.85 3.050 98.34 3.055 98.18 3.076 97.46 15 2.951 98.35 2.874 95.81 3.029 99.04 3.171 94.28 2.874 95.81 20 2.895 96.51 2.936 97.87 2.947 98.23 2.842 94.73 3.149 95.05
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