Binocular vision-based displacement detection method for anchor digging robot
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摘要: 针对掘锚机器人在行驶过程中存在行驶位移检测精度低的问题,以已支护锚杆为定位基准,通过分析掘锚机器人与已支护锚杆之间的距离关系,建立“掘锚机器人−已支护锚杆”定位模型,提出一种基于双目视觉的掘锚机器人行驶位移检测方法。煤矿井下环境复杂,采用传统的Census变换算法得到的视差图具有局限性,通过分析双目视觉测距原理,提出一种改进Census变换算法获取锚杆的视差图,得到锚杆图像的深度信息;提出一种锚杆特征的识别与定位方法,利用边缘检测算法对视差图中的锚杆进行轮廓提取,采用最小外接矩形与最大外接矩形算法对锚杆轮廓进行框选,提取锚杆特征点的像素坐标,通过分析坐标转换关系将特征点像素坐标转换为世界坐标,采用最小二乘法将特征点空间坐标拟合成一条直线,经过该直线建立平行于巷道截面的平面,解算双目相机与该平面之间的距离,进而得到掘锚机器人与该平面之间的距离。搭建移动机器人平台进行掘锚机器人行驶位移检测实验,结果表明:改进后的Census变换算法使误匹配率从19.85%降低到11.52%,较传统Census变换算法的误匹配率降低了41.96%;锚杆特征点识别与定位方法能够有效提取锚杆特征点的空间坐标,经过直线拟合得到相机与3个平行截面之间的距离分别为3 010.428,2 215.910,1 415.127 mm。在机器人定位实验中,将真实计算位移与理论位移进行对比,结果表明,真实计算位移曲线与理论位移曲线基本重合,理论位移与计算位移误差不超过20 mm,可实现掘锚机器人的自主、准确、实时位移检测。
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关键词:
- 掘锚机器人定位 /
- 双目视觉定位 /
- 双目测距 /
- 立体匹配算法 /
- Census变换算法 /
- 锚杆特征点识别 /
- “掘锚机器人−已支护锚杆”定位模型
Abstract: The problem of low detection accuracy of driving displacement exists in the driving process of anchor digging robots. In order to solve the above problem, taking the supporting bolt as the positioning benchmark, by analyzing the distance relationship between the anchor digging robot and the supporting bolt, the positioning model of "anchor digging robot-supported anchor" is established. This paper proposes a binocular vision-based displacement detection method for anchor digging robots. Due to the complexity of the underground coal mine environment, the disparity map obtained by using the traditional Census transform algorithm has limitations. By analyzing the binocular vision ranging principle, an improved Census transform algorithm is proposed to obtain the disparity map of the anchor and the depth information of the anchor image. This paper presents a method of anchor feature recognition and positioning, and uses edge detection algorithm to extract the anchor contour in disparity map. The minimum circumscribed rectangle and the maximum circumscribed rectangle algorithm are used to frame the anchor outline and extract the pixel coordinates of anchor feature points. By analyzing coordinate conversion relationships, the pixel coordinates of feature points are converted to world coordinates. By using the least square method, the spatial coordinates of feature points are fitted into a straight line. The plane parallel to the roadway section is established through the straight line. The distance between the binocular camera and the plane is calculated, and then the distance between the anchor digging robot and the plane is obtained. A mobile robot platform is set up to carry out the displacement detection experiment of the anchor digging robot. The results show the following points. The improved Census transform algorithm reduces the mismatch rate from 19.85% to 11.52%, which is 41.96% lower than the traditional Census transform algorithm. The method of anchor feature point recognition and positioning can effectively extract the spatial coordinates of anchor feature points. The distance between the camera and the three parallel sections is 3 010.428, 2 215.910, 1 415.127 mm respectively through straight line fitting. In the robot positioning experiment, the real calculated displacement is compared with the theoretical displacement. The results show that the real calculated displacement curve coincides with the theoretical displacement curve basically. The error between the theoretical displacement and the calculated displacement is less than 20 mm. The autonomous, accurate and real-time displacement detection of the anchor digging robot can be realized. -
表 1 算法性能对比
Table 1. Comparison of algorithm performance
算法 误匹配率/% 运行时间/s 传统Census变换算法 19.85 4.21 改进Census变换算法 11.52 5.63 表 2 特征点坐标转换结果
Table 2. Coordinate conversion results of feature points
排数 编号 特征点像素
坐标/pixel特征点世界
坐标/mm第
1
排锚杆1 (213,345) (−1012.970,1005.375,
3012.562)2 (265,348) (−514.383,1009.098,
3008.901)3 (313,345) (−8.745,1008.787,
3006.125)4 (369,351) (479.382,1003.812,
3009.351)5 (425,355) (996.156,1011.176,
3015.203)第
2
排锚杆6 (157,391) (−1008.506,1013.185,
2214.812)7 (237,324) (−513.062,1024.289,
2207.625)8 (317,330) (−15.085,1037.623,
2218.191)9 (293,325) (483.410,1025.478,
2215.165)10 (472,329) (979.406,1013.367,
2223.757)第
3
排锚杆11 (143,428) (−1015.214,1031.156,
1410.816)12 (247,420) (−521.467,1013.049,
1415.250)13 (359,414) (−12.891,1016.475,
1417.441)14 (462,413) (507.231,1023.125,
1417.541)15 (575,413) (1051.398,1006.156,
1414.587) -
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