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煤矿水仓自动清理机器人煤泥界面识别方法

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煤矿水仓清淤机器人可以大大提高水仓的清淤效率,保证了水仓的可靠性。在煤矿水仓清淤机器人的研发过程中,煤泥界面的识别成为煤矿水仓清淤机器人研发的关键基础问题,为此,提出了一种二维激光雷达与深度相机结合的方法实现对煤泥界面的准确识别。首先,通过在ROS中对激光雷达和深度相机联合标定的方式获取相机的内部参数和外部参数,建立激光雷达数据和相机数据的变换关系;然后,基于高斯滤波的方式降低深度相机获取的三维点云的噪声,基于均值滤波的方式降低其密度;最后,通过将转换后激光雷达数据与相机数据叠加,通过激光雷达采样点的深度均值对深度相机的三维点云数据进行滤波处理,以实现对煤泥界面的识别。试验结果表明:该方法可以有效识别煤泥界面,为实现水仓清理的智能化、机械化奠定基础。
Sludge interface recognition method of coal mine water sump automatic cleaning robot
The research and development of the coal mine sump dredging robot can greatly improve the dredging efficiency of the water sump and ensure the reliability of the water sump.In the research and development process of the coal mine water sump dredging robot,the identification of the sludge interface has become a key basic problem in the research and development of the coal mine sump dredging robot,and a method combining 2D lidar and depth camera was proposed to achieve accurate identification of the sludge interface.Firstly,the internal parameters and external parameters of the camera were obtained by the joint calibration of the lidar and the depth camera in ROS,and the transformation relationship between the lidar data and the camera data was estab-lished.Then,the noise of the 3D point cloud obtained by the depth camera is reduced by Gaussian filtering,and the density is re-duced by means filtering.Finally,by superimposing the converted lidar data with the camera data,the 3D point cloud data of the depth camera was filtered through the depth mean of the lidar sampling points to realize the identification of the sludge interface.Ex-perimental results show that the method can effectively identify the sludge interface,and lay a foundation for the intelligent and mechanized cleaning of water sump.

coal mine floodwater sump dredging robotcoal sludge interface recognitionpoint cloud overlayROS

贾雪峰、高贵军、李贵虎

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太原理工大学 机械与运载工程学院,山西 太原 030024

山西省矿山流体控制工程实验室,山西 太原 030024

矿山流体控制国家地方联合工程实验室,山西 太原 030024

煤矿水灾 水仓清淤机器人 煤泥界面识别 点云叠加 ROS

2025

煤矿安全
煤炭科学研究总院沈阳研究院

煤矿安全

北大核心
影响因子:0.56
ISSN:1003-496X
年,卷(期):2025.56(1)