针对LiDAR点云数据目标投影几何的非规则性, 提出非规则标识点过程的LiDAR点云数据目标提取方法。首先, 在投影平面上定义随机点过程, 利用其随机点定位该平面上的目标投影, 对每一随机点生成一组节点集以建模该目标投影几何, 作为目标标识; 假设地物目标高程值服从独立同一高斯分布, 从而得到LiDAR点云数据高程测度模型; 在贝叶斯理论架构下建立目标几何提取模型, 并结合可逆跳变马尔可夫链蒙特卡罗(Reversible Jump Markov Chain Monte Carlo, RJMCMC)算法模拟后验分布以及估计各参数; 最后根据最大后验概率准则, 求解最优目标提取模型。采用提出方法对LiDAR点云数据进行目标提取, 根据实验结果可以看出, 算法得到的检测精度均达到80%以上, 最高精度为9943%, 得到了较好的检测结果。本文将传统的规则标识点过程拓展到非规则标识点过程, 可以有效拟合任意形状目标几何。定性和定量的实验结果表明了该方法的可行性、有效性和准确性。

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.