基于改进RANSAC特征提取的掘进装备视觉定位方法研究

为解决煤矿井下低照度、高粉尘、多杂光环境中掘进装备位姿测量不易的问题，提出了一种基于改进随机抽样一致性(RANSAC)特征提取的掘进装备视觉定位方法。首先，对矿用防爆相机采集的三激光标靶图像进行预处理，分别建立形状模型与线框模型；其次，根据形状模型以坐标极值为椭圆模型初始点，将前后两次内点差值比值作为最优迭代次数，迭代求取椭圆模型最优参数获得点特征；然后，根据线框模型以像素坐标模为直线模型初始点，利用自适应条件阈值、抽样次数获得线特征；最后，将点线特征作为三点三线位姿解算模型输入，通过空间坐标变换求得掘进装备位姿信息。实验结果表明，在掘进装备距三激光标靶80 m范围内，所述视觉定位方法的相对误差为±45 mm,可基本满足煤矿井下掘进装备定位需求，为煤矿井下恶劣环境中掘进装备的位姿测量提供一种新的思路。

Research on the visual positioning method of tunneling equipment based on the improved RANSAC feature extraction

To solve the problems of difficult pose measurement of tunneling equipment in low illumination, high dust and multi-lightenvironment in coal mine, a visual positioning method of tunneling equipment based on the improved RANSAC feature extraction isproposed. Firstly, the three laser target images collected by the mine explosion-proof camera are preprocessed, and the shape and wireframe models are established respectively. Then, according to the shape model, the coordinate extremum is taken as the initial point ofthe elliptic model. The ratio of the difference between the previous and the latter two internal points is the optimal number of iterations,and the optimal parameters of the elliptic model are iteratively obtained to extract the point feature. According to the wireframe model,the pixel coordinate model is used as the initial point of the straight line model, and the line features are obtained by using the adaptivecondition threshold and the sampling times. Finally, the point line feature is used as the input of the 3P3L pose solution model, and thepose information of the tunneling equipment is obtained by spatial coordinate transformation. Experimental results show that the relativeerror of the visual positioning method described in this artcle is ±45 mm within the range of 80 m from the three laser target, which canbasically meet the positioning requirements of the coal mine tunneling equipment. It provides a new idea for the pose measurement of thetunneling equipment in the harsh environment of the coal mine.