Accurate Real-Time Localization Estimation in Underground Mine Environments Based on a Distance-Weight Map (DWM)

The precise localization of an underground mine environment is key to achieving unmanned and intelligent underground mining. However, in an underground environment, GPS is unavailable, there are variable and often poor lighting conditions, there is visual aliasing in long tunnels, and the occurrence of airborne dust and water, presenting great difficulty for localization. We demonstrate a high-precision, real-time, without-infrastructure underground localization method based on 3D LIDAR. The underground mine environment map was constructed based on GICP-SLAM, and inverse distance weighting (IDW) was first proposed to implement error correction based on point cloud mapping called a distance-weight map (DWM). The map was used for the localization of the underground mine environment for the first time. The approach combines point cloud frames matching and DWM matching in an unscented Kalman filter fusion process. Finally, the localization method was tested in four underground scenes, where a spatial localization error of 4 cm and 60 ms processing time per frame were obtained. We also analyze the impact of the initial pose and point cloud segmentation with respect to localization accuracy. The results showed that this new algorithm can realize low-drift, real-time localization in an underground mine environment.

Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs

In this study, an autonomous driving robot that drives and returns along a planned route in an underground mine tunnel was developed using a machine-vision-based road sign recognition algorithm. The robot was designed to recognize road signs at the intersection of a tunnel using a geometric matching algorithm of machine vision, and the autonomous driving mode was switched according to the shape of the road sign to drive the robot according to the planned route. The autonomous driving mode recognized the shape of the tunnel using the distance data from the LiDAR sensor; it was designed to drive while maintaining a fixed distance from the centerline or one wall of the tunnel. A machine-vision-based road sign recognition system and an autonomous driving robot for underground mines were used in a field experiment. The results reveal that all road signs were accurately recognized, and the average matching score was 979.14 out of 1000, confirming stable driving along the planned route.