测试环境：

pcl==1.12.1

python-pcl==0.3.1

python==3.7

代码：

# -*- coding: utf-8 -*-
# http://pointclouds.org/documentation/tutorials/planar_segmentation.php#planar-segmentationimport pcl
import numpy as np
import randomdef main():#   pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);##   // Fill in the cloud data#   cloud->width  = 15;#   cloud->height = 1;#   cloud->points.resize (cloud->width * cloud->height);##   // Generate the data#   for (size_t i = 0; i < cloud->points.size (); ++i)#   {#     cloud->points[i].x = 1024 * rand () / (RAND_MAX + 1.0f);#     cloud->points[i].y = 1024 * rand () / (RAND_MAX + 1.0f);#     cloud->points[i].z = 1.0;#   }##   // Set a few outliers#   cloud->points[0].z = 2.0;#   cloud->points[3].z = -2.0;#   cloud->points[6].z = 4.0;###cloud = pcl.PointCloud()points = np.zeros((15, 3), dtype=np.float32)RAND_MAX = 1024.0for i in range(0, 15):points[i][0] = 1024 * random.random() / (RAND_MAX + 1.0)points[i][1] = 1024 * random.random() / (RAND_MAX + 1.0)points[i][2] = 1.0points[0][2] = 2.0points[3][2] = -2.0points[6][2] = 4.0cloud.from_array(points)#   std::cerr << "Point cloud data: " << cloud->points.size () << " points" << std::endl;#   for (size_t i = 0; i < cloud->points.size (); ++i)#     std::cerr << "    " << cloud->points[i].x << " "#                         << cloud->points[i].y << " "#                         << cloud->points[i].z << std::endl;#print('Point cloud data: ' + str(cloud.size) + ' points')for i in range(0, cloud.size):print('x: ' + str(cloud[i][0]) + ', y : ' +str(cloud[i][1]) + ', z : ' + str(cloud[i][2]))#   pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);#   pcl::PointIndices::Ptr inliers (new pcl::PointIndices);#   // Create the segmentation object#   pcl::SACSegmentation<pcl::PointXYZ> seg;#   // Optional#   seg.setOptimizeCoefficients (true);#   // Mandatory#   seg.setModelType (pcl::SACMODEL_PLANE);#   seg.setMethodType (pcl::SAC_RANSAC);#   seg.setDistanceThreshold (0.01);##   seg.setInputCloud (cloud);#   seg.segment (*inliers, *coefficients);#### http://www.pcl-users.org/pcl-SACMODEL-CYLINDER-is-not-working-td4037530.html# NG?# seg = cloud.make_segmenter()# seg.set_optimize_coefficients(True)# seg.set_model_type(pcl.SACMODEL_NORMAL_PLANE)# seg.set_method_type(pcl.SAC_RANSAC)# seg.set_distance_threshold(0.01)# indices, coefficients = seg.segment()seg = cloud.make_segmenter_normals(ksearch=50)seg.set_optimize_coefficients(True)seg.set_model_type(pcl.SACMODEL_NORMAL_PLANE)seg.set_method_type(pcl.SAC_RANSAC)seg.set_distance_threshold(0.01)seg.set_normal_distance_weight(0.01)seg.set_max_iterations(100)indices, coefficients = seg.segment()#   if (inliers->indices.size () == 0)#   {#     PCL_ERROR ("Could not estimate a planar model for the given dataset.");#     return (-1);#   }#   std::cerr << "Model coefficients: " << coefficients->values[0] << " "#                                       << coefficients->values[1] << " "#                                       << coefficients->values[2] << " "#                                       << coefficients->values[3] << std::endl;###if len(indices) == 0:print('Could not estimate a planar model for the given dataset.')exit(0)print('Model coefficients: ' + str(coefficients[0]) + ' ' + str(coefficients[1]) + ' ' + str(coefficients[2]) + ' ' + str(coefficients[3]))#   std::cerr << "Model inliers: " << inliers->indices.size () << std::endl;#   for (size_t i = 0; i < inliers->indices.size (); ++i)#     std::cerr << inliers->indices[i] << "    " << cloud->points[inliers->indices[i]].x << " "#                                                << cloud->points[inliers->indices[i]].y << " "#                                                << cloud->points[inliers->indices[i]].z << std::endl;###print('Model inliers: ' + str(len(indices)))for i in range(0, len(indices)):print(str(indices[i]) + ', x: ' + str(cloud[indices[i]][0]) + ', y : ' +str(cloud[indices[i]][1]) + ', z : ' + str(cloud[indices[i]][2]))if __name__ == "__main__":# import cProfile# cProfile.run('main()', sort='time')main()

运行结果：

Point cloud data: 15 points
x: 0.4073253273963928, y : 0.18234382569789886, z : 2.0
x: 0.6126348376274109, y : 0.07198140025138855, z : 1.0
x: 0.20239822566509247, y : 0.7896735072135925, z : 1.0
x: 0.019813423976302147, y : 0.8002557158470154, z : -2.0
x: 0.560012936592102, y : 0.6460093855857849, z : 1.0
x: 0.11512751877307892, y : 0.29834282398223877, z : 1.0
x: 0.5930672883987427, y : 0.4660494923591614, z : 4.0
x: 0.326516330242157, y : 0.2036980241537094, z : 1.0
x: 0.0727834403514862, y : 0.2508666515350342, z : 1.0
x: 0.6440120935440063, y : 0.9432345628738403, z : 1.0
x: 0.3259447515010834, y : 0.9963557124137878, z : 1.0
x: 0.7400149703025818, y : 0.9620276689529419, z : 1.0
x: 0.16831167042255402, y : 0.2292098104953766, z : 1.0
x: 0.69353848695755, y : 0.7191517353057861, z : 1.0
x: 0.30966588854789734, y : 0.47624409198760986, z : 1.0
Model coefficients: 0.7691919207572937 -0.6329861283302307 -0.08759189397096634 0.18918786942958832
Model inliers: 4
5, x: 0.11512751877307892, y : 0.29834282398223877, z : 1.0
6, x: 0.5930672883987427, y : 0.4660494923591614, z : 4.0
8, x: 0.0727834403514862, y : 0.2508666515350342, z : 1.0
9, x: 0.6440120935440063, y : 0.9432345628738403, z : 1.0