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基于马尔可夫随机场的植被环境中的障碍物识别

Obstacle Recognition in Vegetation Environment Based on Markov Random Field

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摘要

为了识别植被场景中的叶片和相邻障碍物, 提出了一种三维激光雷达的目标检测算法。以雷达点云中的相邻点构建邻域特征, 提取新的特征参数作为判别依据, 采用期望最大算法求得混合高斯模型以表征特征参数的?#26893;记?#20917;; 最后, 利用马尔可夫随机场建立先验模型, 在最大后验概率框架下采用图割法进行求解, 得到最优目标函数。?#30431;?#27861;已成功应用于无人驾驶平台。研究结果表明, ?#30431;?#27861;能有效地识别叶片及其邻接障碍物, 可以清楚地分辨障碍物边界。与传统算法相比, ?#30431;?#27861;具有更高的稳健性和准确率, 且其实时?#26376;?#36275;实际应用的需求。

Abstract

In order to identify foliage and the adjacent obstacles in the vegetation scenes, an object detection algorithm of three-dimensional laser radar is proposed. The neighborhood characteristics of neighboring points are constructed in point cloud, and new characteristic parameters are extracted as determining criterion. Then the Gaussian mixture model is obtained by using the maximum expectation algorithm to characterize the distribution of the parameters. Finally, the priori model is established by using Markov random field. The optimal objective function is obtained by the graph-cut method under the maximum posteriori probability framework. This algorithm has been successfully applied to the unmanned platform. The experimental results show that the algorithm can effectively identify foliage and their adjacent obstacles, and the boundaries of obstacles can be detected clearly. Compared with traditional algorithms, the proposed algorithm is more robust and accurate, and its response time meets the demand of practical applications.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.4

DOI:10.3788/lop56.031010

所属栏目:激光器与激光光学

基金项目:国防预研基金(9140A09031715JB34001)

收稿日期:2018-07-30

修改稿日期:2018-08-22

网络出版日期:2018-08-31

作者单位    点击查看

程子阳:陆军工程大学车辆与电气工程系, 河北 石家庄 050003
任国全:陆军工程大学车辆与电气工程系, 河北 石家庄 050003
张银:陆军工程大学车辆与电气工程系, 河北 石家庄 050003

联系人作者:任国全([email protected])

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引用该论文

Cheng Ziyang,Ren Guoquan,Zhang Yin. Obstacle Recognition in Vegetation Environment Based on Markov Random Field[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031010

程子阳,任国全,张银. 基于马尔可夫随机场的植被环境中的障碍物识别[J]. 激光与光电子学进展, 2019, 56(3): 031010

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