光学精密工程2018，Vol.26Issue(5)：1201-1210,10.DOI:10.3788/OPE.20182605.1201
采用非规则标识点过程的LiDAR点云数据目标提取
Target extraction from LiDAR point cloud data using irregular geometry marked point process
摘要
Abstract
In order to realize the arbitrary shape object extraction from LiDAR point cloud data ,a method based on irregular marked point process was proposed .Firstly ,a random point process was defined on ground plan ,in which random point positioned the object projection on the plan .Then the marks associating individual points were defined with a set of nodes to depict the shape of object on the ground plan .Assumed that the elevation values of ground points followed an independent and identical Gauss distribution ,and that of objects were also characterized by Gauss distributions individually .According to the Bayesian inference ,the object extraction model was obtained ;The RJMCMC algorithm was designed to simulate the posterior distribution and estimate the parameters .Finally , the optimal target extraction model was obtained according to the maximum a posteriori .LiDAR point cloud data was extracted by using the proposed method .According to the experimental results ,it can be seen that the detection accuracy of the algorithm is above 80％ ,the highest accuracy is 99.43％ .In this paper ,the traditional rule mark process is extended to irregular marking process ,and it can be used to fit the geometry of arbitrary shape target effectively .Experimental results show that this method can effectively fit the arbitrary shape objects .关键词
标识点过程/LiDAR点云数据/贝叶斯定理/最大后验概率/可逆跳变马尔可夫链蒙特卡罗算法Key words
Marked Point Process (MPP)/LiDAR point cloud data/Bayesian inference/Maximum A Posteriori (MAP)/Reversible Jump Markov Chain Monte Carlo (RJMCMC)分类
信息技术与安全科学引用本文复制引用
赵泉华,张洪云,李玉..采用非规则标识点过程的LiDAR点云数据目标提取[J].光学精密工程,2018,26(5):1201-1210,10.基金项目
国家自然科学基金青年基金资助项目(No.41301479) （No.41301479）
国家自然科学基金面上项目(No.41271435) （No.41271435）
辽宁省自然科学基金资助项目(No.2015020190) （No.2015020190）
