Improvement of position and posture measurement system for boom-type roadheader based on machine vision
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摘要: 煤矿井下粉尘浓度高、照度低，图像采集质量和特征提取效果受粉尘浓度影响较大，而相机参数和图像处理参数不能根据粉尘浓度变化自适应调整，易产生点−线特征提取不稳定和图像丢帧等问题。针对上述问题，对掘进机位姿视觉检测系统进行改进，利用矿用防爆工业相机采集不同粉尘浓度下的激光点−线图像，通过透过率建立图像灰度值与粉尘浓度等级的关系模型，通过实验获取不同粉尘浓度等级下的最优相机参数和图像处理参数；提出一种参数自适应调整算法，根据粉尘浓度等级自适应调整参数值，提高图像采集质量和点−线特征提取的稳定性和精度，进而提高掘进机位姿视觉检测系统的精度。实验结果表明：改进后悬臂式掘进机位姿视觉检测系统在X，Y，Z方向的平均测量误差分别为28.26，30.58，22.54 mm，处理100张图像后得到的可用图像从75张提高到90张，说明参数自适应调整算法有效提高了图像特征提取精度和数据可用性，从而保证了悬臂式掘进机位姿视觉检测系统的精度和稳定性。Abstract: In coal mine, the dust concentration is high and the illumination is low. The image acquisition quality and characteristic extraction effect are greatly affected by dust concentration. However, the camera parameters and image processing parameters cannot be adjusted adaptively according to the change of dust concentration. Therefore, it is easy to cause problems such as unstable point-line characteristic extraction and image frame loss. In order to solve the above problems, the position and posture measurement system for boom-type roadheader based on machine vision is improved. The mine-used explosion-proof industrial camera is used to collect the laser point-line images under different dust concentrations. The relationship model between the image gray value and the dust concentration level is established through the transmittance. The optimal camera parameters and image processing parameters under different dust concentration levels are obtained through experiments. A parameter adaptive adjustment algorithm is proposed, and the parameter values are adjusted adaptively according to the dust concentration levels. Therefore, the image collection quality and the stability and precision of the point-line characteristic extraction are improved. Moreover, the precision of position and posture measurement system for roadheader based on machine vision is improved. The experimental result show that the average measurement errors in X, Y and Z directions of the improved vision detection system for boom-type roadheader are 28.26 mm, 30.58 mm and 22.54 mm respectively. The number of usable images is increased from 75 to 90 after processing 100 images. These results show that the parameter adaptive adjustment algorithm can effectively improve the precision of image characteristic extraction and the data availability. The algorithm ensures the precision and stability of position and posture measurement system for boom-type roadheader based on machine vision.
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图 1 悬臂式掘进机位姿视觉检测系统
Figure 1. Position and posture measurement system for boom-type roadheader based on machine vision
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图 2 改进后悬臂式掘进机位姿视觉检测系统流程
Figure 2. Flow of the improved position and posture measurement system for boom-type roadheader based on machine vision
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图 3 悬臂式掘进机位姿视觉检测系统实验平台
Figure 3. Experimental platform for position and posture measurement system for boom-type roadheader based on machine vision
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图 4 不同粉尘浓度等级下的激光束图像
Figure 4. Laser beam images under different dust concentration levels
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图 5 掘进机位姿测量误差
Figure 5. The measurement error of the position and posture of the roadheader
表 1 图像灰度值与粉尘浓度等级的关系
Table 1 The relationship between image gray value and dust concentration level
灰度值 透过率/% 粉尘浓度等级 160~200 0~20 超高浓度 120~160 20~40 高浓度 70~120 40~70 中浓度 40~70 70~100 低浓度 
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表 2 中浓度粉尘环境下参数选取实验结果
Table 2 Experimental results of parameter selection in medium-concentration dust environment
曝光时间/μs Smin Vmin 测量值/mm 可用
图像/张X Y Z 40 000 25 25 — — — — 35 — — — — 45 −124.3 9964.1 112.3 65 35 25 −123.3 9959.2 116.3 72 35 −125.2 9962.5 115.1 76 45 −128.1 9969.3 113.3 91 45 25 −125.4 9965.3 110.7 78 35 −126.8 9968.1 112.1 88 45 — — — — 60 000 25 25 — — — — 35 — — — — 45 −125.4 9964.3 110.7 78 35 25 −122.3 9965.6 110.3 80 35 −126.2 9967.8 112.1 84 45 −124.1 9966.6 114.3 88 45 25 −126.4 9967.5 115.7 90 35 −126.1 9969.2 113.1 94 45 — — — — 80 000 25 25 — — — — 35 — — — — 45 — — — — 35 25 — — — — 35 −126.2 9965.5 112.1 75 45 −127.9 9961.2 115.3 79 45 25 — — — — 35 −127.8 9963.1 111.1 84 45 −129.1 9970.3 114.3 80
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表 3 不同粉尘浓度等级下的最优相机和图像处理参数
Table 3 Optimal camera and image processing parameters under different dust concentration levels
粉尘浓度等级 曝光时间/μs Smin Vmin 高浓度 60 000 45 35，45 中浓度 60 000 35，45 35 低浓度 40 000 35 45
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表 4 掘进机位姿检测实验结果
Table 4 The experimental results of the position and posture detection of the roadheader
项目 编号 X/mm Y/mm Z/mm 位姿真实值 1 −100 10 000 −90 2 −200 15 000 −90 3 −300 20 000 −90 4 −350 25 000 −90 5 −420 30 000 −90 位姿测量值
（非自适应调整）1 −135.2 9 963.8 −120.6 2 −169.4 15 039.5 −60.9 3 −339.8 20 039.2 −118.8 4 −312.4 24 958.5 −60.4 5 −458.5 30 042.2 −59.2 位姿测量值
（自适应调整）1 −128.1 9 969.3 −113.3 2 −173.6 15 031.2 −67.5 3 −329.5 20 029.7 −112.1 4 −321.9 24 970.5 −68.9 5 −449.2 30 031.8 −66.3
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表 5 参数自适应与非自适应调整算法误差对比
Table 5 Error comparison between parameter adaptive adjustment algorithm and non-adaptive adjustment algorithm
参数调整
算法最大误差/mm 平均误差/mm 可用
图像/张X 方向 Y 方向 Z 方向 X 方向 Y 方向 Z 方向 非自适应 39.8 42.2 30.8 36.34 39.74 29.78 75 自适应 29.5 31.8 23.7 28.26 30.58 22.54 90
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