Widely used in the computer vision field uniformity various sampling error parameter estimation algorithm is used to exclude samples, different samples corresponding to different applications, for example, eliminate the error registration point, the division point source set on the model, PCL of a random sample consensus algorithm (the RANSAC) as the core, while achieving similar five randomly sampled random parameter estimation algorithm algorithm consistent shape, such as a random sample consensus algorithm (the RANSAC) algorithm consistency maximum likelihood (MLESAC), minimum the median variance consensus algorithm (LMEDS), etc. All estimates are in line with the principle of consistency algorithm parameters. Application of sample consensus algorithm design in PCL main point cloud is divided, depending on the setting of several models, geometrical parameters corresponding to the estimated parameters of the model, the model is divided in a certain allowable range point cloud.
(1) RANSAC random sampling algorithm introduces consistency
RANSAC is "RANdom SAmple Consensus (a random sample of the same)," the abbreviation. Observational data it may contain "outliers" from a group of centralized, parameter mathematical model to estimate an iterative manner. It is an uncertain algorithm - it has a certain probability to arrive at a reasonable result; in order to improve the probability of the need to improve the number of iterations.
Two types of data: user data (inliers) and invalid data (outliers). Small deviation data is called the effective data, a large deviation of the data is invalid data. If a majority of the valid data, valid data is only a small amount, we can be determined by the least squares method or similar methods and the parameters of the error model; many if invalid data (for example, more than 50% of the data are invalid data), the minimum squares on the failure, we need new algorithm
A simple example is to find a suitable two-dimensional straight line from a set of observed data. Suppose observed data points contained within the Bureau and outliers, approximately the point where the straight line through the inning, and a straight line away from the outliers. Simple linear least squares method can not be found within a point to adapt to office, because the least squares method and tried to include all the points, including the outliers. Instead, only the RANSAC can come a point calculated by the Bureau of the model, and the probability is high enough yet. However, RANSAC is no guarantee that the result will correct algorithm in order to ensure a high enough reasonable probability, we must be careful choice of parameters of the algorithm.
(A) contains many outliers in the data set (b) RANSAC find straight line (outliers do not affect the result)
(2) the minimum value method (LMedS)
LMedS approach is very simple, from the sample is randomly selected subset of N samples, a maximum likelihood (usually least squares) deviation for each subset, and the model parameters of the model, the model parameters and the recording of all the subset sample deviation variation that samples centered (i.e. Med deviation), the last N samples of the selected subset Med model parameters corresponding to the minimum deviation as our model parameters to be estimated.
#include <iostream>
#include <pcl/console/parse.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/sample_consensus/sac_model_sphere.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <boost/thread/thread.hpp>
boost::shared_ptr<pcl::visualization::PCLVisualizer>
simpleVis(pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud)
{
// --------------------------------------------
// -----Open 3D viewer and add point cloud-----
// --------------------------------------------
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));
viewer->setBackgroundColor(0, 0, 0);
viewer->addPointCloud<pcl::PointXYZ>(cloud, "sample cloud");
viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "sample cloud");
//viewer->addCoordinateSystem (1.0, "global");
viewer->initCameraParameters();
return (viewer);
}
/*
对点云进行初始化，并对其中一个点云填充点云数据作为处理前的的原始点云，其中大部分点云数据是基于设定的圆球和平面模型计算
而得到的坐标值作为局内点，有1/5的点云数据是被随机放置的，作为局外点。
*/
int
main(int argc, char** argv)
{
// 初始化点云对象
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>); //存储源点云
pcl::PointCloud<pcl::PointXYZ>::Ptr final(new pcl::PointCloud<pcl::PointXYZ>); //存储提取的局内点
// 填充点云数据
cloud->width = 500; //设置填充点云数目
cloud->height = 1; //设置为无序点云
cloud->is_dense = false;
cloud->points.resize(cloud->width * cloud->height);
for (size_t i = 0; i < cloud->points.size(); ++i)
{
if (pcl::console::find_argument(argc, argv, "-s") >= 0 || pcl::console::find_argument(argc, argv, "-sf") >= 0)
{
//根据命令行参数用x^2+y^2+Z^2=1设置一部分点云数据，此时点云组成1/4个球体作为内点
cloud->points[i].x = rand() / (RAND_MAX + 1.0);
cloud->points[i].y = rand() / (RAND_MAX + 1.0);
if (i % 5 == 0)
cloud->points[i].z = rand() / (RAND_MAX + 1.0); //此处对应的点为局外点
else if (i % 2 == 0)
cloud->points[i].z = sqrt(1 - (cloud->points[i].x * cloud->points[i].x)
- (cloud->points[i].y * cloud->points[i].y));
else
cloud->points[i].z = -sqrt(1 - (cloud->points[i].x * cloud->points[i].x)
- (cloud->points[i].y * cloud->points[i].y));
}
else
{ //用x+y+z=1设置一部分点云数据，此时地拿云组成的菱形平面作为内点
cloud->points[i].x = rand() / (RAND_MAX + 1.0);
cloud->points[i].y = rand() / (RAND_MAX + 1.0);
if (i % 2 == 0)
cloud->points[i].z = rand() / (RAND_MAX + 1.0); //对应的局外点
else
cloud->points[i].z = -1 * (cloud->points[i].x + cloud->points[i].y);
}
}
std::vector<int> inliers; //存储局内点集合的点的索引的向量
//创建随机采样一致性对象
pcl::SampleConsensusModelSphere<pcl::PointXYZ>::Ptr
model_s(new pcl::SampleConsensusModelSphere<pcl::PointXYZ>(cloud)); //针对球模型的对象
pcl::SampleConsensusModelPlane<pcl::PointXYZ>::Ptr
model_p(new pcl::SampleConsensusModelPlane<pcl::PointXYZ>(cloud)); //针对平面模型的对象
if (pcl::console::find_argument(argc, argv, "-f") >= 0)
{ //根据命令行参数，来随机估算对应平面模型，并存储估计的局内点
pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_p);
ransac.setDistanceThreshold(.01); //与平面距离小于0.01 的点称为局内点考虑
ransac.computeModel(); //执行随机参数估计
ransac.getInliers(inliers); //存储估计所得的局内点
}
else if (pcl::console::find_argument(argc, argv, "-sf") >= 0)
{
//根据命令行参数 来随机估算对应的圆球模型，存储估计的内点
pcl::RandomSampleConsensus<pcl::PointXYZ> ransac(model_s);
ransac.setDistanceThreshold(.01);
ransac.computeModel();
ransac.getInliers(inliers);
}
// 复制估算模型的所有的局内点到final中
pcl::copyPointCloud<pcl::PointXYZ>(*cloud, inliers, *final);
// 创建可视化对象并加入原始点云或者所有的局内点
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer;
if (pcl::console::find_argument(argc, argv, "-f") >= 0 || pcl::console::find_argument(argc, argv, "-sf") >= 0)
viewer = simpleVis(final);
else
viewer = simpleVis(cloud);
while (!viewer->wasStopped())
{
viewer->spinOnce(100);
boost::this_thread::sleep(boost::posix_time::microseconds(100000));
}
return 0;
}
.\random_sample_consensus.exe
.\random_sample_consensus.exe -f 
.\random_sample_consensus.exe -s
.\random_sample_consensus.exe -sf
