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手写RANSAC实现点云粗配准_ransac粗匹配

作者：AllinToyou | 2024-05-06 09:46:03

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ransac粗匹配

原文链接：https://blog.csdn.net/qq_40301351/article/details/124400499?spm=1001.2101.3001.6650.7&utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7ERate-7-124400499-blog-133255839.235%5Ev43%5Econtrol&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7ERate-7-124400499-blog-133255839.235%5Ev43%5Econtrol&utm_relevant_index=13

算法原理

RANSAC 算法的输入是一组观测值、一个可以解释或拟合到观测值的参数化模型，以及一些置信度参数。

RANSAC 通过迭代选择原始数据的随机子集来实现其目标。这些数据是假设的入值，然后按如下方式测试此假设：

模型拟合到假设的入值，即模型的所有自由参数都是从进值重建的。

然后，根据拟合模型测试所有其他数据，如果某个点与估计的模型非常契合，则也将其视为假设的进一值。

如果有足够的点被归类为假设的入值，则估计的模型是相当好的。

该模型是从所有假设的入值中重新估计的，因为它仅从初始的假设入值集进行估计。

最后，通过估计值相对于模型的误差来评估模型。

此过程重复固定次数，每次生成因点太少而被丢弃的模型，或将优化模型与相应的误差度量一起进行。在后一种情况下，如果优化模型的误差低于上次保存的模型，我们将保留该模型。

流程

  1. 在对应点集中随机选取3个对应点对，并求解刚体变换矩阵。
  2. 计算对应点集中剩余点对在此刚休变换矩阵下的距离误差，若其中一点对的距离误差小于设定的阅值误差，则该点为样本内点，否则为样本外点，并统计前者数目。
  3. 重复以上步骤直至抵达选代次数的上限。统计不同刚体变换模型下的样本内点数量，样本内点数量最多的作为最佳数学模型，保留所有样本内点，剔除样本外点并将剔除外点的对应点对最为正确的点对用于点云配准操作。

优点

  1. RANSAC通常产生更好的结果，即使在无噪声的情况下
  2. RANSAC不需要两个数据集之间的变换的良好初始估计
  3. RANSAC可用于不具有那么多局部特征的数据集

代码实现

ransac法选择对应点，对两组点云进行匹配

// RansacRegistration.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <array>
#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/registration/transformation_estimation_svd.h>
#include <pcl/common/transforms.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <iostream>
#include <vector>

using namespace std;
using namespace pcl;
//计算两点距离的平方
double PointDistance(PointXYZ P1, PointXYZ P2)
{
	double ans = sqrt((P1.x - P2.x)*(P1.x - P2.x) + (P1.y - P2.y)*(P1.y - P2.y) + (P1.z - P2.z)*(P1.z - P2.z));
	return ans;
}
//计算三点是否够远
bool ThreePointsDistanceCheck(PointXYZ P1, PointXYZ P2, PointXYZ P3, int nMinDis)
{
	if (PointDistance(P1, P2) < nMinDis)
		return false;
	if (PointDistance(P1, P3) < nMinDis)
		return false;
	if (PointDistance(P3, P2) < nMinDis)
		return false;
	return true;
}
//点云可视化
void visualize_pcd(PointCloud<PointXYZ>::Ptr pcd_src, PointCloud<PointXYZ>::Ptr pcd_tgt, PointCloud<PointXYZ>::Ptr pcd_final)
{
	//int vp_1, vp_2;
	// Create a PCLVisualizer object
	pcl::visualization::PCLVisualizer viewer("registration Viewer");
	//viewer.createViewPort (0.0, 0, 0.5, 1.0, vp_1);
	// viewer.createViewPort (0.5, 0, 1.0, 1.0, vp_2);
	pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> src_h(pcd_src, 0, 255, 0);
	pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> tgt_h(pcd_tgt, 255, 0, 0);
	pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> final_h(pcd_final, 0, 0, 255);
	viewer.addPointCloud(pcd_src, src_h, "source cloud");
	viewer.addPointCloud(pcd_tgt, tgt_h, "tgt cloud");
	viewer.addPointCloud(pcd_final, final_h, "final cloud");
	//viewer.addCoordinateSystem(1.0);
	while (!viewer.wasStopped())
	{
		viewer.spinOnce(100);
		boost::this_thread::sleep(boost::posix_time::microseconds(100000));
	}

}
int main(int argc, char** argv)
{
	PointCloud<PointXYZ>::Ptr cloud1(new PointCloud<PointXYZ>);
	io::loadPCDFile("e:\\FeatureCloud_sofa.pcd", *cloud1);
	visualize_pcd(cloud1,cloud1,cloud1);

	//读取点云
	char* strTgtFile = new char[80];
	char* strSrcFile = new char[80];
	strcpy(strTgtFile, "e:\\data\\office2.pcd");
	strcpy(strSrcFile, "e:\\data\\office1.pcd");
	PointCloud<PointXYZ>::Ptr CloudTgt(new PointCloud<PointXYZ>);
	PointCloud<PointXYZ>::Ptr CloudSrc(new PointCloud<PointXYZ>);
	io::loadPCDFile(strTgtFile, *CloudTgt);
	io::loadPCDFile(strSrcFile, *CloudSrc);
	int nTgtPoint = CloudTgt->width;//查询点云文件内点的数量
	int nSrcPoint = CloudSrc->width;
	int CorrsSrc[3] = { 0 }, CorrsTgt[3] = { 0 };//对应点对索引
	double MinFitnessScore = 1000000.0;
	double MaxFitnessScore = 1.0;
	registration::TransformationEstimationSVD<PointXYZ, PointXYZ>::Matrix4 FinalTransformation;
	PointCloud<PointXYZ>::Ptr CloudSrcOutput(new PointCloud<PointXYZ>());
	int nIter = 0, nTry = 0;
	srand((unsigned int)time(0));
	for (nTry = 0; nIter < 2000; nTry++)
	{
		//cout << "---------------------------try:" << nTry << endl;
		//源点云随机选取三个符合距离要求的点s1,s2,s3
		int nRand;
		do {
			for (int i = 0; i < 3; i++)
			{
				nRand = rand();
				int p = nRand % (nSrcPoint)+1;
				CorrsSrc[i] = p;
			}
		} while (!ThreePointsDistanceCheck(CloudSrc->points[CorrsSrc[0]], CloudSrc->points[CorrsSrc[1]], CloudSrc->points[CorrsSrc[2]], 1000));
		//cout << "choose from source:\nNo." << CorrsSrc[0] << ":(" << CloudSrc->points[CorrsSrc[0]].x << "," << CloudSrc->points[CorrsSrc[0]].y << "," << CloudSrc->points[CorrsSrc[0]].z << ").\n"
		//	<< "No." << CorrsSrc[1] << ":(" << CloudSrc->points[CorrsSrc[1]].x << "," << CloudSrc->points[CorrsSrc[1]].y << "," << CloudSrc->points[CorrsSrc[1]].z << ").\n"
		//	<< "No." << CorrsSrc[2] << ":(" << CloudSrc->points[CorrsSrc[2]].x << "," << CloudSrc->points[CorrsSrc[2]].y << "," << CloudSrc->points[CorrsSrc[2]].z << ").\n";
		double dDisSrcP0P1 = PointDistance(CloudSrc->points[CorrsSrc[0]], CloudSrc->points[CorrsSrc[1]]);
		double dDisSrcP1P2 = PointDistance(CloudSrc->points[CorrsSrc[1]], CloudSrc->points[CorrsSrc[2]]);
		double dDisSrcP0P2 = PointDistance(CloudSrc->points[CorrsSrc[0]], CloudSrc->points[CorrsSrc[2]]);
		double dDisTgtP0P1 = 0, dDisTgtP1P2 = 0, dDisTgtP0P2 = 0;
		//在目标点云内随机选取一点t1
		{
			nRand = rand();
			int p = nRand % (nTgtPoint)+1;
			CorrsTgt[0] = p;
		}
		//cout << "choose from target:\nNo." << CorrsTgt[0] << ":(" << CloudTgt->points[CorrsTgt[0]].x << "," << CloudTgt->points[CorrsTgt[0]].y << "," << CloudTgt->points[CorrsTgt[0]].z << ").\n";
		//在目标点云随机寻找第二点
		int i = 0;
		vector<int> nTgtP1;
		for (i = 0; i < nTgtPoint; i++)
		{
			dDisTgtP0P1 = PointDistance(CloudTgt->points[i], CloudTgt->points[CorrsTgt[0]]);
			if (fabs(dDisTgtP0P1 - dDisSrcP0P1) < 1)
				nTgtP1.push_back(i);
		}
		if (nTgtP1.size() > 0)
		{
			nRand = rand();
			int p = nRand % (nTgtP1.size());
			CorrsTgt[1] = nTgtP1.at(p);
		}
		nTgtP1.clear();
		//cout << "No." << CorrsTgt[1] << ":(" << CloudTgt->points[CorrsTgt[1]].x << ", " << CloudTgt->points[CorrsTgt[1]].y << ", " << CloudTgt->points[CorrsTgt[1]].z << ").\n";	
		//在目标点云寻找第三点
		i = 0;
		vector<int> nTgtP2;
		for (i = 0; i < nTgtPoint; i++)
		{
			dDisTgtP0P2 = PointDistance(CloudTgt->points[CorrsTgt[0]], CloudTgt->points[i]);
			dDisTgtP1P2 = PointDistance(CloudTgt->points[CorrsTgt[1]], CloudTgt->points[i]);
			if ((fabs(dDisTgtP0P2 - dDisSrcP0P2) < 1) && (fabs(dDisTgtP1P2 - dDisSrcP1P2) < 1))
				nTgtP2.push_back(i);
		}
		if (nTgtP2.size() > 0)
		{
			nRand = rand();
			int p = nRand % (nTgtP2.size());
			CorrsTgt[2] = nTgtP2.at(p);
		}
		nTgtP2.clear();
		//cout << "No." << CorrsTgt[2] << ":(" << CloudTgt->points[CorrsTgt[2]].x << ", " << CloudTgt->points[CorrsTgt[2]].y << ", " << CloudTgt->points[CorrsTgt[2]].z << ").\n";
		if (CorrsTgt[1] == nTgtPoint - 1 || CorrsTgt[2] == nTgtPoint - 1)//目标点云中搜寻到最后一点
			continue;
		if (PointDistance(CloudTgt->points[CorrsTgt[0]], CloudSrc->points[CorrsSrc[0]]) > 500 || PointDistance(CloudTgt->points[CorrsTgt[1]], CloudSrc->points[CorrsSrc[1]]) > 500 || PointDistance(CloudTgt->points[CorrsTgt[2]], CloudSrc->points[CorrsSrc[2]]) > 500)
			continue;//对应点距离过大
		if (CorrsTgt[0] == CorrsTgt[1] || CorrsTgt[0] == CorrsTgt[2] || CorrsTgt[1] == CorrsTgt[2])
			continue;
		cout << "-----------------------------iteration:" << nIter++ << endl;
		//计算旋转矩阵
		PointCloud<PointXYZ>::Ptr CloudPointSrc(new PointCloud<PointXYZ>());
		PointCloud<PointXYZ>::Ptr CloudPointTgt(new PointCloud<PointXYZ>());
		CloudPointSrc->width = 3;
		CloudPointSrc->height = 1;
		CloudPointSrc->is_dense = false;
		CloudPointSrc->resize(CloudPointSrc->width * CloudPointSrc->height);
		CloudPointTgt->width = 3;
		CloudPointTgt->height = 1;
		CloudPointTgt->is_dense = false;
		CloudPointTgt->resize(CloudPointTgt->width * CloudPointTgt->height);
		//同名点写入点云
		CloudPointSrc->points[0] = CloudSrc->points[CorrsSrc[0]];
		CloudPointSrc->points[1] = CloudSrc->points[CorrsSrc[1]];
		CloudPointSrc->points[2] = CloudSrc->points[CorrsSrc[2]];
		CloudPointTgt->points[0] = CloudTgt->points[CorrsTgt[0]];
		CloudPointTgt->points[1] = CloudTgt->points[CorrsTgt[1]];
		CloudPointTgt->points[2] = CloudTgt->points[CorrsTgt[2]];
		//利用SVD方法求解变换矩阵
		registration::TransformationEstimationSVD<PointXYZ, PointXYZ> TESVD;
		registration::TransformationEstimationSVD<PointXYZ, PointXYZ>::Matrix4 transformation;
		TESVD.estimateRigidTransformation(*CloudPointSrc, *CloudPointTgt, transformation);
		cout << "estimated rotation:" << endl;
		printf("    | %6.3f %6.3f %6.3f | \n", transformation(0, 0), transformation(0, 1), transformation(0, 2));
		printf("R = | %6.3f %6.3f %6.3f | \n", transformation(1, 0), transformation(1, 1), transformation(1, 2));
		printf("    | %6.3f %6.3f %6.3f | \n", transformation(2, 0), transformation(2, 1), transformation(2, 2));
		printf("\n");
		printf("t = < %0.3f, %0.3f, %0.3f >\n", transformation(0, 3), transformation(1, 3), transformation(2, 3));
		//旋转源点云,计算匹配分数
		double FitnessScore = 0.0;
		transformPointCloud(*CloudSrc, *CloudSrcOutput, transformation);
		KdTreeFLANN<PointXYZ> kdtree;
		kdtree.setInputCloud(CloudTgt);
		int K = 10;
		std::vector<int> nn_indices(K);
		std::vector<float> nn_dists(K);
		int nr = 0;
		for (i = 0; i < CloudSrcOutput->points.size(); i = i + 4)
		{
			// Find its nearest neighbor in the target
			kdtree.nearestKSearch(CloudSrcOutput->points[i], 1, nn_indices, nn_dists);
			// Deal with occlusions (incomplete targets)//部分被遮挡
			if (nn_dists[0] <= 5000)
			{
				// Add to the fitness score
				FitnessScore += nn_dists[0];
				nr++;
			}
		}
		if (nr > 0)
			FitnessScore = (FitnessScore / nr);
		else
			FitnessScore = std::numeric_limits<double>::max();
		cout << "FitnessScore is:" << FitnessScore << endl;
		if (FitnessScore < MinFitnessScore)
		{
			MinFitnessScore = FitnessScore;
			FinalTransformation = transformation;
		}
	}
	//输出最终旋转矩阵
	transformPointCloud(*CloudSrc, *CloudSrcOutput, FinalTransformation);
	FILE* pfOutputTransformation;
	char* strOutputTransformation = new char[80];
	strcpy(strOutputTransformation, "e:\\Transformation.txt");
	fopen_s(&pfOutputTransformation, strOutputTransformation, "w");
	if (pfOutputTransformation == NULL) return 1;
	fprintf(pfOutputTransformation, "Min Fitness Score is:%f\n", MinFitnessScore);
	fprintf(pfOutputTransformation, "estimated rotation of \"%s\" and \"%s\" :\n", strTgtFile, strSrcFile);
	fprintf(pfOutputTransformation, "    | %6.3f %6.3f %6.3f | \n", FinalTransformation(0, 0), FinalTransformation(0, 1), FinalTransformation(0, 2));
	fprintf(pfOutputTransformation, "R = | %6.3f %6.3f %6.3f | \n", FinalTransformation(1, 0), FinalTransformation(1, 1), FinalTransformation(1, 2));
	fprintf(pfOutputTransformation, "    | %6.3f %6.3f %6.3f | \n", FinalTransformation(2, 0), FinalTransformation(2, 1), FinalTransformation(2, 2));
	fprintf(pfOutputTransformation, "\n");
	fprintf(pfOutputTransformation, "t = < %0.3f, %0.3f, %0.3f >\n", FinalTransformation(0, 3), FinalTransformation(1, 3), FinalTransformation(2, 3));
	fclose(pfOutputTransformation);
	delete[] strOutputTransformation;
	strOutputTransformation = NULL;
	delete[] strTgtFile;
	strTgtFile = NULL;
	delete[] strSrcFile;
	strSrcFile = NULL;
	//输出旋转后源点云+目标点云
	io::savePCDFileASCII("e:\\OutputCloud.pcd", *CloudSrcOutput);
	visualize_pcd(CloudSrc, CloudTgt, CloudSrcOutput);
	return 0;
}

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结果展示

在这里插入图片描述

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