Research On Data Processing And Application Of Cave Point Cloud Based On Ground 3D Laser

The cave landscape mainly records the formation process of karst landforms and provides the place for it’s preservation and research.The cave landscape under the karst landform not only has the geological research significance,but also attractive in tourist attractions.Zhijindong,located in Zhijin County,Guizhou Province,is awarded the fame of “China’s Six Most Beautiful Tourist Caves”.In order to strengthen the research,protection and development of cave landscape,the use of 3D laser scanning technology to establish a digital 3D model has certain theoretical significance and economic practical value.In this thesis,the author does the work of data preprocessing and model reconstruction of Zhijindong cave hole cloud by using ground 3D laser scanner technology and the methods of field mapping,algorithm design and simulation experiments.Focusing on the cloud processing and modeling technology of karst caves,the key problems of point cloud data filtering and denoising,data reduction and model reconstruction are studied in depth.The main research is as follows:(1)A point cloud data filtering algorithm based on the aspect ratio.When the 3D point cloud data is collected,the measured data will have small noise points or outliers due to factors such as the measuring instrument,the external environment and the measured object itself,which will affect the data processing efficiency.The reconstructed model will also be rough and even deformed.In order to effectively filter out noise points,the point cloud data denoising algorithm of adaptive layering technique and string height ratio is put forward.Experimental analysis and comparison show that the algorithm can effectively remove noise data,and can improve the processing efficiency while maintaining the detailed data and geometric features of the model.(2)A point cloud data reduction algorithm for point-to-fit surface distance.As the scanning instrument is continuously optimized and upgraded,its data acquisition capability is gradually improved,making large-scale complex scene modeling work possible.Therefore,the model data to be processed is getting larger and larger,and streamlined compression is an indispensable step in data processing.For the problem of streamhole cloud data simplification,the local point fitting of the data points,and then the feature point cloud is retained according to the point-to-fit surface distance discrimination method,and the adaptive layering technology in the rapid prototyping field is also used to realize the large scene.Experiments show that this method avoids the shortcomings of traditional algorithms that focus on retaining features but low compression ratio or pursuit of compression ratio but neglects detailed features.The effect of compression ratio and feature of point cloud data has achieved a good balance and faster Simplified speed.(3)After the pre-processing of point cloud data under the above-mentioned series,the problem of model reconstruction of point cloud is deeply discussed and experimentally studied.Based on the analysis of algorithms such as MC reconstruction,greedy projection triangulation reconstruction and Poisson reconstruction.The PCL_visualization module in the PCL open source library visualizes the reconstruction model,analyzes and evaluates the reconstruction models of different algorithms.In conclusion,this thesis studies the processing of catalyzed point cloud data and 3D model reconstruction.The experimental results show that the proposed filtering and simplification algorithm has feasibility and superiority in point cloud data processing.Based on this,the 3D model visualization of the landscape of the cave is carried out,and the overall experiment is effective.