An Algorithm for Generating Multi-Axis NC Machining Tool-Path on Scattered Point Cloud

2010 ◽  
Vol 139-141 ◽  
pp. 1322-1327
Author(s):  
Dian Zhu Sun ◽  
Jian Liu ◽  
Yan Rui Li ◽  
Zong Wei Niu
Keyword(s):  
Differential Geometry ◽  
Spanning Tree ◽  
Point Cloud ◽  
Tool Path ◽  
Minimum Spanning Tree ◽  
Reference Data ◽  
Nc Machining ◽  
Spatial Index ◽  
Local Reference ◽  
Normal Vectors

To achieve the accurate duplication of product in reverse engineering, we proposed an algorithm for generating multi-axis NC machining tool-path directly based on scattered point cloud. At first, we obtained the neighborhoods of tool-path section based on the dynamic spatial index of scattered point cloud and from which we selected the matching points. Then the tool-path section points can be obtained through intersection of the tool-path section and the lines which link the matching points, and the normal vectors of tool-path section points can be calculated based on the differential geometry property of local reference data. Finally we computed the cutter location points by means of projection method and sorted them based on the minimum spanning tree algorithm to form the tool-path. With the results of several experiments, the high precision and efficiency of this algorithm are verified.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Geological Modeling Method Based on the Normal Dynamic Estimation of Sparse Point Clouds

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1819
Author(s):  
Tiandong Shi ◽  
Deyun Zhong ◽  
Liguan Wang
Keyword(s):  
Point Cloud ◽  
Minimum Spanning Tree ◽  
Point Clouds ◽  
Original Method ◽  
Modeling Method ◽  
Geological Modeling ◽  
Improved Method ◽  
Normal Estimation ◽  
Sampling Points ◽  
Correct Point

The effect of geological modeling largely depends on the normal estimation results of geological sampling points. However, due to the sparse and uneven characteristics of geological sampling points, the results of normal estimation have great uncertainty. This paper proposes a geological modeling method based on the dynamic normal estimation of sparse point clouds. The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clouds; (3) using an implicit function to construct a geological model. The innovation of this method is an iterative estimation of the point cloud normal. The geological engineer adjusts the normal direction of some point clouds according to the geological law, and then the method uses these correct point cloud normals as a reference to estimate the normals of all point clouds. By continuously repeating the iterative process, the normal estimation result will be more accurate. Experimental results show that compared with the original method, the improved method is more suitable for the normal estimation of sparse point clouds by adjusting normals, according to prior knowledge, dynamically.

scholarly journals Converting MST to TSP Path by Branch Elimination

2020 ◽  
Vol 11 (1) ◽  
pp. 177
Author(s):  
Pasi Fränti ◽  
Teemu Nenonen ◽  
Mingchuan Yuan
Keyword(s):  
Spanning Tree ◽  
Closed Loop ◽  
Minimum Spanning Tree ◽  
Travelling Salesman Problem ◽  
Open Loop ◽  
Travelling Salesman ◽  
Elimination Algorithm ◽  
Number Of Iterations ◽  
Number Of Branches

Travelling salesman problem (TSP) has been widely studied for the classical closed loop variant but less attention has been paid to the open loop variant. Open loop solution has property of being also a spanning tree, although not necessarily the minimum spanning tree (MST). In this paper, we present a simple branch elimination algorithm that removes the branches from MST by cutting one link and then reconnecting the resulting subtrees via selected leaf nodes. The number of iterations equals to the number of branches (b) in the MST. Typically, b << n where n is the number of nodes. With O-Mopsi and Dots datasets, the algorithm reaches gap of 1.69% and 0.61 %, respectively. The algorithm is suitable especially for educational purposes by showing the connection between MST and TSP, but it can also serve as a quick approximation for more complex metaheuristics whose efficiency relies on quality of the initial solution.

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