A Steady Arm Slope Detection Method Based on 3D Point Cloud Segmentation

Aiming at the geometric parameters detection of catenary support structure, a non-contact steady arm slope detection method based on 3D point cloud segmentation is put forward in this paper. First, obtain 3D point cloud through virtual reality technology. Then, use R-RANSAC (Region-Random Sample Consensus) algorithm and Euclidean clustering to extract multiple linear regions of catenary support system after setting extraction range of point cloud data according to the characteristics of catenary. Statistical filter is utilized to remove the outliers to avoid the recognition disturbance. Finally, calculate horizontal angle and slope of catenary steady arm though space vector information of these liner regions. By comparing the detection results with standard model, experimental result shows the detection method is accurate.