0. 简介
最近在看PCL滤波配准等操作,之前在自动驾驶-激光雷达预处理/特征提取和提到了一些滤除点云等操作,但是最近作者发现里面还有一些配准的方法还没有提到,所以这里重新开个章节来给大家列举一些常用的滤波方式,方便大家查阅和使用
1. 滤波&聚类
1.1 直通滤波器
void pass_through_filter(const pcl::PointCloud<pcl::PointXYZRGB>::Ptr &input_cloud) //直通滤波器
{
std::cout << "start pass_through_filter" << std::endl;
calc_sight_center(); //计算视点中心,视点中心为滤波器的输入参数
// void ex_segmentor::calc_sight_center()
// {
// double roll, pitch, yaw;
// tf::Quaternion quat_tmp;
// tf::quaternionMsgToTF(latest_camera_pos_.pose.pose.orientation, quat_tmp);
// tf::Matrix3x3(quat_tmp).getRPY(roll, pitch, yaw);
// centerX_ = latest_camera_pos_.pose.pose.position.x + gaze_length_ * cos(yaw);
// centerY_ = latest_camera_pos_.pose.pose.position.y + gaze_length_ * sin(yaw);
// centerZ_ = latest_camera_pos_.pose.pose.position.z - gaze_length_ * sin(pitch);
// }
// build the condition
pcl::ConditionAnd<pcl::PointXYZRGB>::Ptr range_limit(new pcl::ConditionAnd<pcl::PointXYZRGB>); //构建范围限制条件
range_limit->addComparison(pcl::FieldComparison<pcl::PointXYZRGB>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZRGB>("x", pcl::ComparisonOps::GT, centerX_ - 1.5))); // x坐标大于视点中心x坐标-1.5
range_limit->addComparison(pcl::FieldComparison<pcl::PointXYZRGB>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZRGB>("x", pcl::ComparisonOps::LT, centerX_ + 1.5))); // x坐标小于视点中心x坐标+1.5
range_limit->addComparison(pcl::FieldComparison<pcl::PointXYZRGB>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZRGB>("y", pcl::ComparisonOps::GT, centerY_ - 1.5))); // y坐标大于视点中心y坐标-1.5
range_limit->addComparison(pcl::FieldComparison<pcl::PointXYZRGB>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZRGB>("y", pcl::ComparisonOps::LT, centerY_ + 1.5))); // y坐标小于视点中心y坐标+1.5
range_limit->addComparison(pcl::FieldComparison<pcl::PointXYZRGB>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZRGB>("z", pcl::ComparisonOps::GT, centerZ_ - 1.5))); // z坐标大于视点中心z坐标-1.5
range_limit->addComparison(pcl::FieldComparison<pcl::PointXYZRGB>::ConstPtr(new pcl::FieldComparison<pcl::PointXYZRGB>("z", pcl::ComparisonOps::LT, centerZ_ + 1.5))); // z坐标小于视点中心z坐标+1.5
//构建滤波器
pcl::ConditionalRemoval<pcl::PointXYZRGB> condrem; //构建滤波器
condrem.setCondition(range_limit); //设置滤波条件
condrem.setInputCloud(input_cloud); //设置输入点云
//滤波操作
condrem.filter(*input_cloud);
}
1.2 离群点滤波器
void statical_outlier_filter(const pcl::PointCloud<PointXYZRGB>::Ptr &input_cloud, int nr_k, double stddev_mult) //滤波器移除离群点
{
pcl::StatisticalOutlierRemoval<PointXYZRGB> sorfilter(true); //构建滤波器
sorfilter.setInputCloud(input_cloud);
sorfilter.setMeanK(nr_k); //设置在进行统计时考虑的临近点个数
sorfilter.setStddevMulThresh(stddev_mult); //设置判断是否为离群点的阀值,用来倍乘标准差,也就是上面的stddev_mult
sorfilter.filter(*input_cloud); //滤波结果存储到cloud_filtered
}
1.3 体素化滤波器
void voxel_filter(const pcl::PointCloud<PointXYZRGB>::Ptr &input_cloud, float resolution) //体素化滤波器
{
pcl::VoxelGrid<PointXYZRGB> voxel_grid; //构建体素化滤波器
voxel_grid.setInputCloud(input_cloud); //设置输入点云
voxel_grid.setLeafSize(resolution, resolution, resolution); //设置体素的大小
voxel_grid.filter(*input_cloud); //滤波结果存储到cloud_filtered
}
1.4 平面点滤除
bool remove_plane(const pcl::PointCloud<PointXYZRGB>::Ptr &input_cloud, const Eigen::Vector3f &axis, double plane_thickness) //移除平面
{
pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients); //平面参数矩阵
pcl::PointIndices::Ptr inliers(new pcl::PointIndices); //平面内点索引
// Create the segmentation object
pcl::SACSegmentation<pcl::PointXYZRGB> seg; //构建分割对象
seg.setOptimizeCoefficients(true); //设置是否优化系数
seg.setModelType(pcl::SACMODEL_PERPENDICULAR_PLANE); //设置模型类型为平面
seg.setMethodType(pcl::SAC_RANSAC); //设置分割方法为RANSAC
seg.setMaxIterations(500); //设置最大迭代次数
seg.setAxis(axis); //设置分割轴
seg.setEpsAngle(0.25); //设置角度阈值
seg.setDistanceThreshold(plane_thickness); //设置距离阈值 0.025 0.018
seg.setInputCloud(input_cloud); //设置输入点云
seg.segment(*inliers, *coefficients); //分割平面
if (inliers->indices.size() < 500)
{
// ROS_INFO("plane size is not enough large to remove.");
return false;
}
pcl::ExtractIndices<pcl::PointXYZRGB> extract;
extract.setInputCloud(input_cloud); //设置输入点云
extract.setIndices(inliers); //设置索引,用来滤除
extract.setNegative(true); //设置是否滤除索引内的点
extract.filter(*input_cloud);
return true;
}
1.5 RGBD颜色特征聚类
void clustoring_with_color(pcl::PointCloud<pcl::PointXYZRGB>::Ptr &input_cloud, std::vector<pcl::PointCloud<PointXYZRGB>::Ptr> &clusters, int min_cluster_size, float distance_th, float color_th, float region_color_th, unsigned int num_nbr) //根据点云的颜色完成聚类
{
std::vector<pcl::PointIndices> clusters_indices; //聚类索引
pcl::search::KdTree<pcl::PointXYZRGB>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZRGB>); //构建kd树
kdtree->setInputCloud(input_cloud); //设置输入点云
// 基于颜色的区域生长聚类对象
pcl::RegionGrowingRGB<pcl::PointXYZRGB> clustering;
clustering.setInputCloud(input_cloud);
clustering.setSearchMethod(kdtree); //设置搜索方法
// 这里,最小簇大小也会影响后处理步骤: 小于这个值的clusters_indices将与邻点合并。
clustering.setMinClusterSize(min_cluster_size); //设置最小簇大小
// 设置距离阈值,以知道哪些点将被视为,邻点
clustering.setDistanceThreshold(distance_th); // 1
// 颜色阈值,用于比较两个点的RGB颜色
clustering.setPointColorThreshold(color_th); // 9 6.5 25.0f 18.0f
// 后处理步骤的区域颜色阈值:颜色在阈值内的clusters_indices将合并为一个。
clustering.setRegionColorThreshold(region_color_th); // 2
//区域耦合时检查的附近的数量。 默认为100, 在不影响结果的范围内适度设定小范围。
clustering.setNumberOfRegionNeighbours(num_nbr); //设置近邻数量
// clustering.setSmoothModeFlag(true);
// clustering.setSmoothnessThreshold(0.95);
clustering.extract(clusters_indices); //提取聚类索引
for (std::vector<pcl::PointIndices>::const_iterator i = clusters_indices.begin(); i != clusters_indices.end(); ++i)//遍历聚类索引
{
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cluster(new pcl::PointCloud<pcl::PointXYZRGB>); //构建聚类点云
for (std::vector<int>::const_iterator pit = i->indices.begin(); pit != i->indices.end(); ++pit) //遍历聚类索引中的点索引
{
cluster->points.push_back(input_cloud->points[*pit]); //将点添加到聚类点云
}
cluster->width = cluster->points.size();
cluster->height = 1;
cluster->is_dense = true;
clusters.push_back(cluster); //将聚类点云添加到聚类点云集合中
}
}
2. 匹配器
2.1 ICP点云精配准
template <typename PointCloudPtr>
bool ex_segmentor::icp_registration(PointCloudPtr &input_obj, PointCloudPtr &input_scene, PointCloudPtr &output_obj, Eigen::Matrix4f &result_transform, float &result_error, uint max_iteration, float max_distance, float ransac_th) // icp匹配
{
pcl::IterativeClosestPoint<PointXYZRGB, PointXYZRGB> icp; // icp对象
icp.setInputSource(input_obj); //设置输入源点云
icp.setInputTarget(input_scene); //设置输入目标点云
// Set the max correspondence distance to 5cm (e.g., correspondences with higher distances will be ignored)
icp.setMaxCorrespondenceDistance(max_distance); //设置最大匹配距离 0.05
// Set the maximum number of iterations (criterion 1)
icp.setMaximumIterations(max_iteration); //设置最大迭代次数 50
// Set the transformation epsilon (criterion 2)
// icp.setTransformationEpsilon (1e-5); //1e-8
// Set the euclidean distance difference epsilon (criterion 3)
// icp.setEuclideanFitnessEpsilon (0.000001); //1
icp.setRANSACOutlierRejectionThreshold(ransac_th); //设置RANSAC阈值
icp.align(*output_obj); // icp匹配
if (icp.hasConverged()) //如果icp匹配成功
{
result_transform = icp.getFinalTransformation(); //获取最终变换矩阵
result_error = icp.getFitnessScore(); //获取最终匹配误差
return true;
}
else
{
result_transform = Eigen::Matrix4f::Identity(4, 4);
result_error = 1.0;
return false;
}
}
/**
icp_registration(object_aligned, cluster, Final, icp_result_transform, icp_error);
**/
2.2 FPFH点云粗配准
void FPFH_generation(pcl::PointCloud<PointXYZRGB>::Ptr &input, FPFHCloud::Ptr &output) // FPFH特征提取
{
// 首先,生成法线
pcl::NormalEstimationOMP<PointNormal, PointNormal> nest; // OMP线程数
pcl::PointCloud<PointNormal>::Ptr temp(new pcl::PointCloud<PointNormal>); //构建暂时点云
pcl::copyPointCloud(*input, *temp); //拷贝点云
nest.setRadiusSearch(0.01); //设置半径搜索范围
nest.setInputCloud(temp); //设置输入点云
nest.compute(*temp); //计算暂时点云
// 然后生成FPFH点云
pcl::FPFHEstimationOMP<PointNormal, PointNormal, FPFH> fest; // OMP线程数
fest.setRadiusSearch(0.01); // 0.025
fest.setInputCloud(temp);
fest.setInputNormals(temp);
fest.compute(*output);
}
template <typename PointType, typename PointCloudPtr>
bool FPFH_matching(PointCloudPtr &object, FPFHCloud::Ptr &object_feature, PointCloudPtr &scene, FPFHCloud::Ptr &scene_feature, PointCloudPtr &result_cloud, Eigen::Matrix4f &result_transformation) // FPFH粗配准方法
{
pcl::SampleConsensusPrerejective<PointType, PointType, FPFH> align; //采样一致性预排除算法
align.setInputSource(object); //设置输入源点云
align.setSourceFeatures(object_feature); //设置输入源特征点云
align.setInputTarget(scene); //设置输入目标点云
align.setTargetFeatures(scene_feature); //设置输入目标特征点云
align.setMaximumIterations(5000); // 设置最大迭代次数
align.setNumberOfSamples(7); // 设置采样点数
align.setCorrespondenceRandomness(10); // 设置随机匹配点数
align.setSimilarityThreshold(0.5f); // 设置相似度阈值
align.setMaxCorrespondenceDistance(0.01f); // 设置最大匹配距离
align.setInlierFraction(0.05f); // 设置内点比例
align.align(*result_cloud);
if (align.hasConverged())
{
result_transformation = align.getFinalTransformation(); //获取最终变换矩阵
// pcl::console::print_info("Inliers: %i/%i , %i\n", align.getInliers().size(), scene->size(), object->size());
// return (float(align.getInliers().size()) / float(object->size()));
return true;
}
// return 0.0f;
return false;
}
/**
FPFHCloud::Ptr cluster_feature(new FPFHCloud);
FPFH_generation(cluster, cluster_feature);
ROS_INFO("cluster_size : %d, feature size : %d", cluster->size(), cluster_feature->size());
bool FPFH_match_success = FPFH_matching<PointXYZRGB>(object_, object_feature_, cluster, cluster_feature, object_aligned, align_result_transform);
**/
2.3 PCA点云粗配准
void ex_segmentor::PCA_registration(pcl::PointCloud<PointXYZRGB>::Ptr &input_obj, pcl::PointCloud<PointXYZRGB>::Ptr &input_scene, pcl::PointCloud<PointXYZRGB>::Ptr &projected_obj, Eigen::Matrix4f &result_transform) // PCA粗配准
{
using PointType = PointXYZRGB; //点类型
pcl::PCA<PointType> pca; // PCA算法
pcl::PointCloud<PointType> objProj;
pca.setInputCloud(input_obj); //设置输入点云
pca.project(*input_obj, objProj); //投影点云
Eigen::Matrix3f EigenSpaceObj = pca.getEigenVectors(); //获取特征向量
// std::cout << pca.getMean() << std::endl;
Eigen::Vector3f PcaTransObj(pca.getMean()(0), pca.getMean()(1), pca.getMean()(2)); //获取平均值
Eigen::Matrix4f transform_obj; //变换矩阵
transform_obj << EigenSpaceObj(0, 0), EigenSpaceObj(0, 1), EigenSpaceObj(0, 2), PcaTransObj(0),
EigenSpaceObj(1, 0), EigenSpaceObj(1, 1), EigenSpaceObj(1, 2), PcaTransObj(1),
EigenSpaceObj(2, 0), EigenSpaceObj(2, 1), EigenSpaceObj(2, 2), PcaTransObj(2),
0, 0, 0, 1; //设置变换矩阵
std::cout << transform_obj << std::endl;
Eigen::Matrix3f EigenSpaceObjT = EigenSpaceObj.transpose(); //获取特征向量的转置矩阵
Eigen::Vector3f PcaTransObj_inv = -EigenSpaceObjT * PcaTransObj; //获取平均值的逆矩阵
Eigen::Matrix4f transform_obj_inv;
transform_obj_inv << EigenSpaceObjT(0, 0), EigenSpaceObjT(0, 1), EigenSpaceObjT(0, 2), PcaTransObj_inv(0),
EigenSpaceObjT(1, 0), EigenSpaceObjT(1, 1), EigenSpaceObjT(1, 2), PcaTransObj_inv(1),
EigenSpaceObjT(2, 0), EigenSpaceObjT(2, 1), EigenSpaceObjT(2, 2), PcaTransObj_inv(2),
0, 0, 0, 1;
std::cout << transform_obj_inv << std::endl;
pcl::PointCloud<PointType> sceneProj; //投影点云
pca.setInputCloud(input_scene); //设置输入点云
pca.project(*input_scene, sceneProj); //投影点云
Eigen::Matrix3f EigenSpaceScene = pca.getEigenVectors(); //获取特征向量
Eigen::Vector4f PcaTransScene = pca.getMean(); //获取平均值
Eigen::Matrix4f transform_scene;
// std::cout << pca.getMean() << std::endl;
transform_scene << EigenSpaceScene(0, 0), EigenSpaceScene(0, 1), EigenSpaceScene(0, 2), PcaTransScene(0),
EigenSpaceScene(1, 0), EigenSpaceScene(1, 1), EigenSpaceScene(1, 2), PcaTransScene(1),
EigenSpaceScene(2, 0), EigenSpaceScene(2, 1), EigenSpaceScene(2, 2), PcaTransScene(2),
0, 0, 0, 1;
std::cout << transform_scene << std::endl;
result_transform = transform_scene * transform_obj_inv; //计算最终变换矩阵
pcl::transformPointCloud(*input_obj, *projected_obj, result_transform); //变换点云
return;
};
/**
PCA_registration(object_, cluster, object_aligned, align_result_transform);
**/
参考链接
https://blog.csdn.net/fei_12138/article/details/118677416
https://github.com/TsuruMasato/OnlineObjectDetector/blob/master/src/ex_segmentor.cpp
https://blog.csdn.net/aliexken/article/details/123892377
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