[PCL]2 点云法向量计算NormalEstimation

从GitHub的代码版本库下载源代码https://github.com/PointCloudLibrary/pcl，用CMake生成VS项目，查看PCL的源码位于pcl_features项目下1.Feature类：template class Feature : public PCLBase注意 Feature是一个泛型类，有一个compute方法。 1 template void pcl::Feature::compute (PointCloudOut &output)
2 {
3 if (!initCompute ())
4 {
5 output.width = output.height = 0;
6 output.points.clear ();
7 return;
8 }
9 // Copy the header
10 output.header = input_->header;
11 // Resize the output dataset
12 if (output.points.size () != indices_->size ())
13 output.points.resize (indices_->size ());
14 // Check if the output will be computed for all points or only a subset
15 // If the input width or height are not set, set output width as size
16 if (indices_->size () != input_->points.size () || input_->width * input_->height == 0)
17 {
18 output.width = static_cast (indices_->size ());
19 output.height = 1;
20 }
21 else
22 {
23 output.width = input_->width;
24 output.height = input_->height;
25 }
26 output.is_dense = input_->is_dense;
27 // Perform the actual feature computation
28 computeFeature (output);
29 deinitCompute ();
30 }2.注意computeFeature (output);方法 ，可以知道这是一个私有的虚方法。 private: /** \brief Abstract feature estimation method. * \param[out] output the resultant features */ virtual void computeFeature (PointCloudOut &output) = 0;3.查看Feature的继承关系可以知道template class NormalEstimation: public FeatureNormalEstimation类是Feature模板类的子类，因此执行的是NormalEstimation类的computeFeature方法。查看computeFeature方法： 1 template void pcl::NormalEstimation::computeFeature (PointCloudOut &output)
2 {
3 // Allocate enough space to hold the results
4 // \note This resize is irrelevant for a radiusSearch ().
5 std::vector< int> nn_indices (k_);
6 std::vector< float> nn_dists (k_);
7 output.is_dense = true;
8 // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
9 if (input_->is_dense)
10 {
11 // Iterating over the entire index vector
12 for (size_t idx = 0; idx < indices_->size (); ++idx)
13 {
14 if (this ->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0 ||
15 !computePointNormal (*surface_, nn_indices, output.points[idx].normal[0], output.points[idx].normal[1], output.points[idx].normal[2], output.points[idx].curvature))
16 {
17 output.points[idx].normal[0] = output.points[idx].normal[1] = output.points[idx].normal[2] = output.points[idx].curvature = std::numeric_limits::quiet_NaN ();
18 output.is_dense = false;
19 continue;
20 }
21
22 flipNormalTowardsViewpoint (input_->points[(*indices_)[idx]], vpx_, vpy_, vpz_, output.points[idx].normal[0], output.points[idx].normal[1], output.points[idx].normal[2]);
23 }
24 }
25 else
26 {
27 // Iterating over the entire index vector
28 for (size_t idx = 0; idx < indices_->size (); ++idx)
29 {
30 if (!isFinite ((*input_)[(*indices_)[idx]]) ||
31 this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0 ||
32 !computePointNormal (surface_, nn_indices, output.points[idx].normal[0], output.points[idx].normal[1], output.points[idx].normal[2], output.points[idx].curvature))
33 {
34 output.points[idx].normal[0] = output.points[idx].normal[1] = output.points[idx].normal[2] = output.points[idx].curvature = std::numeric_limits::quiet_NaN ();
35
36 output.is_dense = false;
37 continue;
38 }
39 flipNormalTowardsViewpoint (input_->points[(indices_)[idx]], vpx_, vpy_, vpz_, output.points[idx].normal[0], output.points[idx].normal[1], output.points[idx].normal[2]);
40 }
41 }
42 }4.因此分析NormalEstimation的算法流程如下： 　 （1）进行点云的初始化initCompute　　（2）初始化计算结果输出对象output　　（3）计算点云法向量，具体由子类的computeFeature方法实现。先搜索近邻searchForNeighbors ，然后计算computePointNormal　　　　采用的方法是PCA主成分分析法。　　　　参考文献：《Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments》 P45-49　　　　　　　　　　http://www.pclcn.org/study/shownews.php?lang=cn&id=105　　　　点云的法向量主要是通过点所在区域的局部拟合的表面进行计算。平面通过一个点和法向量进行表示。对于每一个点Pi,对应的协方差矩阵C　　　　　　　　关于主成份分析的基本原理和算法流程参考：http://blog.csdn.net/lming_08/article/details/21335313　　（4）flipNormalTowardsViewpoint 法向量定向，采用方法是：使法向量的方向朝向viewpoint。5.NormalEstimation模板类的重载方法computeFeature分析，computePointNormal分析。 1 inline bool computePointNormal (const pcl::PointCloud &cloud, const std::vector &indices,
2 float &nx, float &ny, float &nz, float &curvature)
3 {
4 if (indices.size () < 3 ||
5 computeMeanAndCovarianceMatrix (cloud, indices, covariance_matrix_, xyz_centroid_) == 0)
6 {
7 nx = ny = nz = curvature = std::numeric_limits::quiet_NaN ();
8 return false;
9 }
10
11 // Get the plane normal and surface curvature
12 solvePlaneParameters (covariance_matrix_, nx, ny, nz, curvature);
13 return true;
14 }computeMeanAndCovarianceMatrix主要是PCA过程中计算平均值和协方差矩阵，在类centroid.hpp中。
而solvePlaneParameters方法则是为了求解特征值和特征向量。代码见feature.hpp。具体实现时采用了pcl::eigen33方法。 1 inline void pcl::solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix,
2 float &nx, float &ny, float &nz, float &curvature)
3 {
4 // Avoid getting hung on Eigen’s optimizers
5 // for (int i = 0; i < 9; ++i)
6 // if (!pcl_isfinite (covariance_matrix.coeff (i)))
7 // {
8 // //PCL_WARN ("[pcl::solvePlaneParameteres] Covariance matrix has NaN/Inf values!\n");
9 // nx = ny = nz = curvature = std::numeric_limits::quiet_NaN ();
10 // return;
11 // }
12 // Extract the smallest eigenvalue and its eigenvector
13 EIGEN_ALIGN16 Eigen::Vector3f::Scalar eigen_value;
14 EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
15 pcl::eigen33 (covariance_matrix, eigen_value, eigen_vector);
16
17 nx = eigen_vector [0];
18 ny = eigen_vector [1];
19 nz = eigen_vector [2];
20
21 // Compute the curvature surface change
22 float eig_sum = covariance_matrix.coeff (0) + covariance_matrix.coeff (4) + covariance_matrix.coeff (8);
23 if (eig_sum != 0)
24 curvature = fabsf (eigen_value / eig_sum);
25 else
26 curvature = 0;
27 } 6.法向量定向见normal_3d.h文件中，有多个覆写方法。摘其一： 1 / \brief Flip (in place) the estimated normal of a point towards a given viewpoint
2 * \param point a given point
3 * \param vp_x the X coordinate of the viewpoint
4 * \param vp_y the X coordinate of the viewpoint
5 * \param vp_z the X coordinate of the viewpoint
6 * \param nx the resultant X component of the plane normal
7 * \param ny the resultant Y component of the plane normal
8 * \param nz the resultant Z component of the plane normal
9 * \ingroup features
10 */
11 template inline void
12 flipNormalTowardsViewpoint (const PointT &point, float vp_x, float vp_y, float vp_z,
13 float &nx, float &ny, float &nz)
14 {
15 // See if we need to flip any plane normals
16 vp_x -= point.x;
17 vp_y -= point.y;
18 vp_z -= point.z;
19
20 // Dot product between the (viewpoint - point) and the plane normal
21 float cos_theta = (vp_x * nx + vp_y * ny + vp_z * nz);
22
23 // Flip the plane normal
24 if (cos_theta < 0)
25 {
26 nx *= -1;
27 ny *= -1;
28 nz *= -1;
29 }
30 }运行的实例结果：

转载 http://www.cnblogs.com/yhlx125/p/5137850.html