Method for detecting the status of retaining walls in intelligent mines

During the driving process of unmanned vehicles in mines, if the retaining wall in the mining area is damaged and not detected and repaired in a timely manner, the vehicle may exceed the safety range of the retaining wall during driving or unloading. It can easily cause safety accidents. The existing methods for detecting the status of retaining walls are mostly based on point cloud data collected by vehicle and drone sensing devices. The methods have limited field of view, high sparsity and poor stability. There is a lack of detection methods for the integrity status of retaining walls. In order to solve the above problems, a method for detecting the integrity of retaining wall status based on roadside LiDAR sensors is proposed. A high-resolution roadside LiDAR sensor is used to collect point cloud data of the retaining wall in the driving area of the vehicle. Polygonal area filtering and voxel rasterization are used to obtain complete point cloud data of the retaining wall. A sliding trace search technique is used to divide the retaining wall into sub units along its extension direction to accommodate the different shaped retaining walls. In response to the problem of false detection caused by uneven mining sites and sparse remote point cloud data, a dual threshold method of height difference threshold and density threshold is adopted. It detects the integrity of the entire retaining wall status by detecting the defects of sub units. The method collects point cloud data of "L" and "S" type retaining walls in a mining area in Inner Mongolia. The on-site experiments are conducted in both occluded and unobstructed scenarios. The results show that this detection method has strong detection capability for defects in different shapes of retaining walls. The method can identify and mark the damaged parts of point cloud data in real-time.