The repair of drone point cloud potholes based on the α-shape and SSA-XGBoost algorithms
Aiming at the problems of difficult selection of core hyperparameters,difficult identification of point cloud hole repair range and low hole repair accuracy when using Extreme Gradient Boosting algorithm for UAV point cloud hole repair,this paper proposes a point cloud hole repair method based on Sparrow Search Algorithm to optimize the limit gradient lifting tree.Firstly,the α-shape algorithm is used to identify the holes in the point cloud.The position information of the holes in the point cloud and the surrounding point cloud is obtained and used as the input sample of the model.Using Sparrow search algorithm to optimize the core hyperparameters in the limit gradient lifting tree algorithm,the SSA-XGBoost point cloud hole repair model is constructed,and the model is applied to the repair of UAV point cloud holes.Finally,the prediction accuracy of SSA-XGBoost is compared with XGBoost and BP neural network.The experimental results show that the prediction of SSA-XGBoost model is more accurate than the other two algorithms,which has certain significance in point cloud hole repair.
国家科技期刊平台
NSTL
万方数据
