Extraction of Road Point Cloud in Open Pit Based on Multi-feature Constraints

Aiming at the problem that road point cloud data in open pit is difficult to be accurately extracted through point cloud features such as normal vector and kerb，a method of road point cloud extraction in open pit with multi‑feature constraints was proposed. Taking the laser point cloud in the open pit of Qianshan limestone mine in Liaoyang City as the data source，the original data was downsampled firstly，and then the training set and verification set were made and divided based on the five kinds of point cloud features including single point RGB information，neighborhood RGB information，neighborhood height difference，neighborhood roughness，and reflection intensity. The road point cloud extraction model was constructed and optimized using the random forest algorithm. Furthermore，European clustering algorithm was introduced to improve the road point cloud extraction model. Finally，the road point cloud extraction results were evaluated in open pit. The results show that the proposed method can effectively and accurately extract the road point cloud data in open pit in real time. © 2024 Northeast University. All rights reserved.