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Method for automatically reconstructing tree structure from ground-based laser radar point cloud

A lidar and tree technology, applied in the field of tree reconstruction, can solve the problems of wrong topological connection of branches and trunks, incomplete structure, loss of tree crown information, etc., and achieve the effect of high geometric accuracy and topological fidelity

Pending Publication Date: 2022-03-22
BEIJING FORESTRY UNIVERSITY
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AI Technical Summary

Problems solved by technology

Due to the sensitivity to point cloud noise or point cloud integrity, the loss of canopy information caused by dimensionality reduction operations will lead to wrong branch topological connections or incomplete structures.

Method used

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  • Method for automatically reconstructing tree structure from ground-based laser radar point cloud
  • Method for automatically reconstructing tree structure from ground-based laser radar point cloud
  • Method for automatically reconstructing tree structure from ground-based laser radar point cloud

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Embodiment Construction

[0036] A method for automatically reconstructing tree branch structures based on ground-based laser radar point clouds, the specific implementation method is:

[0037] 1. Filter and denoise the original input point cloud. The improved asymptotically encrypted triangulation filter algorithm is used to classify ground points, the digital terrain model is established through irregular triangulation interpolation algorithm, the normalization of sample point clouds is completed, and the single tree segmentation method is used to obtain single tree instances.

[0038] 2. Enter the single wood point cloud as input data into the model.

[0039] 3. Process the input point cloud of a tree based on the Delaunay triangulation of Delaunay, and find the Minimum Spanning Tree (MST) between the edges of the Delaunay triangulation of the points.

[0040] 4. Extract the initialization skeleton of a single tree. After getting the triangulation graph, all edges are weighted using the lengths of...

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Abstract

The invention provides a method for automatically reconstructing a tree branch structure from a ground-based laser radar point cloud, which is characterized in that in order to accurately fit a tree branch geometric structure and a topological relation from a TLS point cloud, a single tree geometric structure and topology are reconstructed on the basis of a graph theory method in combination with the advantages of prior hypothesis modeling and lightweight expression; the method for reconstructing the fine geometric structure of the individual tree branches and trunks is high in universality and precision. Firstly, triangulation of an input point cloud is completed based on three-dimensional Delaunay, and a shortest path of a directed weighted graph is calculated based on a Dijkstra algorithm. And then extracting an initial tree skeleton by using a minimum spanning tree (MST) algorithm framework, designing a merging algorithm of redundant vertexes and edges, and completing simplification and optimization of the initial skeleton. And finally, based on the tree skeleton, single tree branch structure cylinder fitting and an optimization algorithm thereof are completed, and a fine single tree branch geometric structure is reconstructed.

Description

1. Technical field [0001] The invention relates to a tree reconstruction method, mainly a method for automatically reconstructing a tree branch structure from a ground-based laser radar point cloud. [0002] 2. Technical Background [0003] With the rise of LiDAR software and hardware technology and the gradual reduction in cost, it has been continuously studied and applied in the field of forest ecology. LiDAR point cloud can describe detailed tree geometry information, which provides a data basis for tree modeling with high precision, strong sense of reality and multi-level detail expression. In recent years, some researchers at home and abroad have tried and explored the theory, method and technology of tree modeling based on lidar point cloud, and achieved results that have more forestry advantages than traditional tree modeling methods. At present, it mainly includes clustering thought modeling, graph theory modeling, prior hypothesis modeling, Laplacian modeling and li...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T17/20G06T5/00G06T17/05G06T15/00
CPCG06T17/20G06T17/05G06T15/005G06T2207/10028G06T2207/20024G06T5/70
Inventor 范光鹏卢昊张珈玮
Owner BEIJING FORESTRY UNIVERSITY
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