本文共 2424 字，大约阅读时间需要 8 分钟。
C++程序运行闪退解决方法：
1.主函数中return 0;前加getchar();需要头文件<iostream>
2.主函数return 0;前加system("pause");
其中代码中在main（）函数中添加return true前加了getchar();
注意：代码中提示无法找到源文件pch.h,可以将其注释掉，对代码运行不产生影响！
代码如下：
// PointCloudsDis.cpp : 此文件包含 "main" 函数。程序执行将在此处开始并结束。////#include "pch.h"#include #include #include #include #include #include #include #include #include using namespace std;double computerCloudResolution(const pcl::PointCloud ::ConstPtr cloud) { double res = 0.0; int n_points = 0; int nres; std::vector indices(2); std::vector sqr_distances(2); pcl::search::KdTree tree; tree.setInputCloud(cloud); for (size_t i = 0; i < cloud->size(); ++i) { if (!std::isfinite((*cloud)[i].x)) { continue; } nres = tree.nearestKSearch(i, 2, indices, sqr_distances); if (nres == 2) { res += sqrt(sqr_distances[1]); ++n_points; } } if (n_points != 0) { res /= n_points; } return res; }int main(){ ifstream data; data.open("knee_piece.TXT", ios::in); int rows = -1; char buf[200]; while (!data.eof()) { data.getline(buf, sizeof(buf)); rows++; } data.clear(); data.seekg(0, ios::beg); //pcl::PointCloud cloud; pcl::PointCloud ::Ptr cloud(new pcl::PointCloud ); cloud->width = rows; cloud->height = 1; cloud->is_dense = false; cloud->resize(cloud->width*cloud->height); for (int i = 0; i < rows; i++) { double num[3]; data >> num[0]; data >> num[1]; data >> num[2]; cloud->points[i].x = num[0]; cloud->points[i].y = num[1]; cloud->points[i].z = num[2]; } double res = computerCloudResolution(cloud); cout << "distance=" << res << endl << "rows=" << rows; getchar(); //防止窗口闪退 return true;}
温馨提示：
积分下载名字叫“基于PCL的点云平均间距计算”，其实真没什么意思，就是以下这段代码，其实和上文中的一样的，况且代码中仅有计算点云间距的代码！下载链接：
不要冤枉浪费积分！
#include float means_re(pcl::PointCloud ::Ptr cloud){ float res = 0.0; int n_points = 0; int nres; std::vector indices(2); std::vector sqr_distances(2); pcl::KdTreeFLANN tree; tree.setInputCloud(cloud); for (size_t i = 0; i < cloud->size(); ++i) { if (!pcl_isfinite(cloud->points[i].x)) { continue; } //Considering the second neighbor since the first is the point itself. nres = tree.nearestKSearch(i, 2, indices, sqr_distances); if (nres == 2) { res += sqrt(sqr_distances[1]); ++n_points; } } if (n_points != 0) { res /= n_points; } //std::cout <<"平均距离："< <
参考博文：
1、
2、
转载地址：http://ogdo.baihongyu.com/