双层优化的激光雷达点云场景分割方法

doi:10.11947/j.AGCS.2018.20170493

测绘学报 ›› 2018, Vol. 47 ›› Issue (2): 269-274.doi: 10.11947/j.AGCS.2018.20170493

双层优化的激光雷达点云场景分割方法

李明磊¹, 刘少创², 杨欢³, 亓晨³

1. 南京航空航天大学电子信息工程学院, 江苏南京 211106;
2. 中国科学院遥感与数字地球研究所, 北京 100101;
3. 武汉大学测绘学院, 湖北武汉 430079

收稿日期:2017-09-01 修回日期:2017-11-29 出版日期:2018-02-20 发布日期:2018-03-02
作者简介:李明磊(1987-),男,博士,讲师,研究方向为数字摄影测量与遥感。E-mail:minglei_li@126.com
基金资助:
江苏省自然科学基金（BK20170781）

Bilevel Optimization for Scene Segmentation of LiDAR Point Cloud

LI Minglei¹, LIU Shaochuang², YANG Huan³, QI Chen³

1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
3. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Received:2017-09-01 Revised:2017-11-29 Online:2018-02-20 Published:2018-03-02
Supported by:
The Natural Science Foundation of Jiangsu Province of China (No. BK20170781)

摘要/Abstract

摘要： 对激光雷达扫描的非结构化点云进行分割处理，是进行数据组织、重构和信息提取的重要步骤。本文根据点云表面的局部可微的性质，提出了一种递进形式的双层优化分割算法。首先在黎曼几何框架下计算点的拓扑关系和距离度量特性，以k均值聚类的方法获得过分割体素，作为底层分割结果。然后，将点云的体素模式化为节点，构建最小生成树，提取节点的高级特征信息，利用图优化得到对点云细节自适应的区域分割效果。通过真实数据进行验证，并与现有方法比较，证明所提算法的可行性和先进性。

关键词: 点云分割, 黎曼几何, 超体素, 最小生成树, 特征提取

Abstract: The segmentation of point clouds obtained by light detection and ranging (LiDAR) systems is a critical step for many tasks,such as data organization,reconstruction and information extraction.In this paper,we propose a bilevel progressive optimization algorithm based on the local differentiability.First,we define the topological relation and distance metric of points in the framework of Riemannian geometry,and in the point-based level using k-means method generates over-segmentation results,e.g.super voxels.Then these voxels are formulated as nodes which consist a minimal spanning tree.High level features are extracted from voxel structures,and a graph-based optimization method is designed to yield the final adaptive segmentation results.The implementation experiments on real data demonstrate that our method is efficient and superior to state-of-the-art methods.

Key words: point cloud segmentation, Riemannian geometry, super voxel, minimal spanning tree, feature extraction

中图分类号:

P237

李明磊, 刘少创, 杨欢, 亓晨. 双层优化的激光雷达点云场景分割方法[J]. 测绘学报, 2018, 47(2): 269-274.

LI Minglei, LIU Shaochuang, YANG Huan, QI Chen. Bilevel Optimization for Scene Segmentation of LiDAR Point Cloud[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2): 269-274.

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双层优化的激光雷达点云场景分割方法

Bilevel Optimization for Scene Segmentation of LiDAR Point Cloud

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