7：C++搭配PCL点云配准之3DSC特征
作者：互联网
代码：
1 #include <pcl/point_types.h>
2 #include <pcl/point_cloud.h>
3 #include <pcl/features/normal_3d.h>
4 #include <pcl/features/3dsc.h>
5 #include <pcl/search/kdtree.h>
6 #include <pcl/io/pcd_io.h>
7 #include <pcl/filters/random_sample.h>//采取固定数量的点云
8 #include <pcl/registration/ia_ransac.h>//采样一致性
9 #include <pcl/registration/icp.h>//icp配准
10 #include <boost/thread/thread.hpp>
11 #include <pcl/visualization/pcl_visualizer.h>//可视化
12 #include <time.h>//时间
13
14 using pcl::NormalEstimation;
15 using pcl::search::KdTree;
16 typedef pcl::PointXYZ PointT;
17 typedef pcl::PointCloud<PointT> PointCloud;
18
19 //点云可视化
20 void visualize_pcd2(PointCloud::Ptr pcd_src, PointCloud::Ptr pcd_tgt, PointCloud::Ptr pcd_src1, PointCloud::Ptr pcd_tgt1)
21 {
22
23 //创建初始化目标
24 pcl::visualization::PCLVisualizer viewer("registration Viewer");
25 int v1(0);
26 int v2(1);
27 viewer.createViewPort(0.0, 0.0, 0.5, 1.0, v1);
28 viewer.createViewPort(0.5, 0.0, 1.0, 1.0, v2);
29 pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> src_h(pcd_src, 0, 255, 0);
30 pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> tgt_h(pcd_tgt, 255, 0, 0);
31 pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> src_h1(pcd_src1, 0, 255, 0);
32 pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> tgt_h1(pcd_tgt1, 255, 0, 0);
33 viewer.setBackgroundColor(255, 255, 255);
34 viewer.addPointCloud(pcd_src, src_h, "source cloud", v1);
35 viewer.addPointCloud(pcd_tgt, tgt_h, "tgt cloud", v1);
36 viewer.addPointCloud(pcd_src1, src_h1, "source cloud1", v2);
37 viewer.addPointCloud(pcd_tgt1, tgt_h1, "tgt cloud1", v2);
38
39 //viewer.addCoordinateSystem(0.05);
40 while (!viewer.wasStopped())
41 {
42 viewer.spinOnce(100);
43 boost::this_thread::sleep(boost::posix_time::microseconds(100000));
44 }
45 }
46 //由旋转平移矩阵计算旋转角度
47 void matrix2angle(Eigen::Matrix4f &result_trans, Eigen::Vector3f &result_angle)
48 {
49 double ax, ay, az;
50 if (result_trans(2, 0) == 1 || result_trans(2, 0) == -1)
51 {
52 az = 0;
53 double dlta;
54 dlta = atan2(result_trans(0, 1), result_trans(0, 2));
55 if (result_trans(2, 0) == -1)
56 {
57 ay = M_PI / 2;
58 ax = az + dlta;
59 }
60 else
61 {
62 ay = -M_PI / 2;
63 ax = -az + dlta;
64 }
65 }
66 else
67 {
68 ay = -asin(result_trans(2, 0));
69 ax = atan2(result_trans(2, 1) / cos(ay), result_trans(2, 2) / cos(ay));
70 az = atan2(result_trans(1, 0) / cos(ay), result_trans(0, 0) / cos(ay));
71 }
72 result_angle << ax, ay, az;
73
74 cout << "x轴旋转角度：" << ax << endl;
75 cout << "y轴旋转角度：" << ay << endl;
76 cout << "z轴旋转角度：" << az << endl;
77 }
78
79
80 int main(int argc, char** argv)
81 {
82 //加载点云文件
83 PointCloud::Ptr cloud_src_o(new PointCloud);//原点云，待配准
84 pcl::io::loadPCDFile("ear.pcd", *cloud_src_o);
85 PointCloud::Ptr cloud_tgt_o(new PointCloud);//目标点云
86 pcl::io::loadPCDFile("earzhuan05.pcd", *cloud_tgt_o);
87
88 clock_t start = clock();
89
90 //去除NAN点
91 std::vector<int> indices_src; //保存去除的点的索引
92 pcl::removeNaNFromPointCloud(*cloud_src_o, *cloud_src_o, indices_src);
93 std::cout << "remove *cloud_src_o nan" << endl;
94
95 std::vector<int> indices_tgt;
96 pcl::removeNaNFromPointCloud(*cloud_tgt_o, *cloud_tgt_o, indices_tgt);
97 std::cout << "remove *cloud_tgt_o nan" << endl;
98
99 //采样固定的点云数量
100 pcl::RandomSample<PointT> rs_src;
101 rs_src.setInputCloud(cloud_src_o);
102 rs_src.setSample(550);
103 PointCloud::Ptr cloud_src(new PointCloud);
104 rs_src.filter(*cloud_src);
105 std::cout << "down size *cloud_src_o from " << cloud_src_o->size() << "to" << cloud_src->size() << endl;
106
107 pcl::RandomSample<PointT> rs_tgt;
108 rs_tgt.setInputCloud(cloud_tgt_o);
109 rs_tgt.setSample(550);
110 PointCloud::Ptr cloud_tgt(new PointCloud);
111 rs_tgt.filter(*cloud_tgt);
112 std::cout << "down size *cloud_tgt_o from " << cloud_tgt_o->size() << "to" << cloud_tgt->size() << endl;
113
114 //计算表面法线
115 pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne_src;
116 ne_src.setInputCloud(cloud_src);
117 pcl::search::KdTree< pcl::PointXYZ>::Ptr tree_src(new pcl::search::KdTree< pcl::PointXYZ>());
118 ne_src.setSearchMethod(tree_src);
119 pcl::PointCloud<pcl::Normal>::Ptr cloud_src_normals(new pcl::PointCloud< pcl::Normal>);
120 ne_src.setRadiusSearch(4);
121 ne_src.compute(*cloud_src_normals);
122
123 pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne_tgt;
124 ne_tgt.setInputCloud(cloud_tgt);
125 pcl::search::KdTree< pcl::PointXYZ>::Ptr tree_tgt(new pcl::search::KdTree< pcl::PointXYZ>());
126 ne_tgt.setSearchMethod(tree_tgt);
127 pcl::PointCloud<pcl::Normal>::Ptr cloud_tgt_normals(new pcl::PointCloud< pcl::Normal>);
128 //ne_tgt.setKSearch(20);
129 ne_tgt.setRadiusSearch(4);
130 ne_tgt.compute(*cloud_tgt_normals);
131
132 //计算3dsc
133 pcl::ShapeContext3DEstimation<pcl::PointXYZ, pcl::Normal, pcl::ShapeContext1980> sp_tgt;
134 sp_tgt.setInputCloud(cloud_tgt);
135 sp_tgt.setInputNormals(cloud_tgt_normals);
136 //kdTree加速
137 pcl::search::KdTree<PointT>::Ptr tree_tgt_sp(new pcl::search::KdTree<PointT>);
138 sp_tgt.setSearchMethod(tree_tgt_sp);
139 pcl::PointCloud<pcl::ShapeContext1980>::Ptr sps_tgt(new pcl::PointCloud<pcl::ShapeContext1980>());
140 sp_tgt.setRadiusSearch(4);
141 sp_tgt.compute(*sps_tgt);
142
143 cout << "compute *cloud_tgt_sps" << endl;
144
145 pcl::ShapeContext3DEstimation<pcl::PointXYZ, pcl::Normal, pcl::ShapeContext1980> sp_src;
146 sp_src.setInputCloud(cloud_src);
147 sp_src.setInputNormals(cloud_src_normals);
148 //kdTree加速
149 pcl::search::KdTree<PointT>::Ptr tree_src_sp(new pcl::search::KdTree<PointT>);
150 sp_src.setSearchMethod(tree_src_sp);
151 pcl::PointCloud<pcl::ShapeContext1980>::Ptr sps_src(new pcl::PointCloud<pcl::ShapeContext1980>());
152 sp_src.setRadiusSearch(4);
153 sp_src.compute(*sps_src);
154
155 cout << "compute *cloud_tgt_sps" << endl;
156
157
158
159 //SAC配准
160 pcl::SampleConsensusInitialAlignment<pcl::PointXYZ, pcl::PointXYZ, pcl::ShapeContext1980> scia;
161 scia.setInputSource(cloud_src);
162 scia.setInputTarget(cloud_tgt);
163 scia.setSourceFeatures(sps_src);
164 scia.setTargetFeatures(sps_tgt);
165 //scia.setMinSampleDistance(1);
166 //scia.setNumberOfSamples(2);
167 //scia.setCorrespondenceRandomness(20);
168 PointCloud::Ptr sac_result(new PointCloud);
169 scia.align(*sac_result);
170 std::cout << "sac has converged:" << scia.hasConverged() << " score: " << scia.getFitnessScore() << endl;
171 Eigen::Matrix4f sac_trans;
172 sac_trans = scia.getFinalTransformation();
173 std::cout << sac_trans << endl;
174 //pcl::io::savePCDFileASCII("bunny_transformed_sac.pcd", *sac_result);
175 clock_t sac_time = clock();
176
177 //icp配准
178 PointCloud::Ptr icp_result(new PointCloud);
179 pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
180 icp.setInputSource(cloud_src);
181 icp.setInputTarget(cloud_tgt_o);
182 //Set the max correspondence distance to 4cm (e.g., correspondences with higher distances will be ignored)
183 icp.setMaxCorrespondenceDistance(8);
184 // 最大迭代次数
185 icp.setMaximumIterations(100);
186 // 两次变化矩阵之间的差值
187 icp.setTransformationEpsilon(1e-10);
188 // 均方误差
189 icp.setEuclideanFitnessEpsilon(0.01);
190 icp.align(*icp_result, sac_trans);
191
192 clock_t end = clock();
193 cout << "total time: " << (double)(end - start) / (double)CLOCKS_PER_SEC << " s" << endl;
194
195 cout << "sac time: " << (double)(sac_time - start) / (double)CLOCKS_PER_SEC << " s" << endl;
196 cout << "icp time: " << (double)(end - sac_time) / (double)CLOCKS_PER_SEC << " s" << endl;
197
198 std::cout << "ICP has converged:" << icp.hasConverged()
199 << " score: " << icp.getFitnessScore() << std::endl;
200 Eigen::Matrix4f icp_trans;
201 icp_trans = icp.getFinalTransformation();
202 //cout<<"ransformationProbability"<<icp.getTransformationProbability()<<endl;
203 std::cout << icp_trans << endl;
204 //使用创建的变换对未过滤的输入点云进行变换
205 pcl::transformPointCloud(*cloud_src_o, *icp_result, icp_trans);
206 //保存转换的输入点云
207 //pcl::io::savePCDFileASCII("_transformed_sac_ndt.pcd", *icp_result);
208
209 //计算误差
210 Eigen::Vector3f ANGLE_origin;
211 Eigen::Vector3f TRANS_origin;
212 ANGLE_origin << 0, 0, M_PI / 4;
213 TRANS_origin << 0, 0.3, 0.2;
214 double a_error_x, a_error_y, a_error_z;
215 double t_error_x, t_error_y, t_error_z;
216 Eigen::Vector3f ANGLE_result;
217 matrix2angle(icp_trans, ANGLE_result);
218 a_error_x = fabs(ANGLE_result(0)) - fabs(ANGLE_origin(0));
219 a_error_y = fabs(ANGLE_result(1)) - fabs(ANGLE_origin(1));
220 a_error_z = fabs(ANGLE_result(2)) - fabs(ANGLE_origin(2));
221 cout << "点云实际旋转角度:\n" << ANGLE_origin << endl;
222 cout << "x轴旋转误差 : " << a_error_x << " y轴旋转误差 : " << a_error_y << " z轴旋转误差 : " << a_error_z << endl;
223
224 cout << "点云实际平移距离:\n" << TRANS_origin << endl;
225 t_error_x = fabs(icp_trans(0, 3)) - fabs(TRANS_origin(0));
226 t_error_y = fabs(icp_trans(1, 3)) - fabs(TRANS_origin(1));
227 t_error_z = fabs(icp_trans(2, 3)) - fabs(TRANS_origin(2));
228 cout << "计算得到的平移距离" << endl << "x轴平移" << icp_trans(0, 3) << endl << "y轴平移" << icp_trans(1, 3) << endl << "z轴平移" << icp_trans(2, 3) << endl;
229 cout << "x轴平移误差 : " << t_error_x << " y轴平移误差 : " << t_error_y << " z轴平移误差 : " << t_error_z << endl;
230
231 //可视化
232 visualize_pcd2(cloud_src_o, cloud_tgt_o, icp_result, cloud_tgt_o);
233 return (0);
234 }
标签：src,配准,tgt,3DSC,C++,result,pcl,PointCloud,cloud 来源： https://www.cnblogs.com/beautifulmoonlight/p/14951732.html