std::sort引发的core (这个分析还是很不错的!!!)

本文介绍了一个由std::sort引发的段错误问题,并通过一个示例程序进行了复现。分析了STL排序算法中分区函数的实现细节,指出了比较函数在元素相等时返回true可能导致的内存访问越界问题。

        转载地址:http://blog.chinaunix.net/uid-23146151-id-3066266.html

 

        这两天定位了一个由std::sort引发的core。


写了下面的程序来复现此问题。
  1. #include <stdio.h>
  2. #include <vector>
  3. #include <algorithm>
  4. #include <new>
  5.  
  6. struct foo_t
  7. {
  8.     int size;
  9. };
  10.  
  11. class cmp_t
  12. {
  13. public:
  14.     bool operator()(foo_t *a, foo_t *b)
  15.     {
  16.         return a->size >= b->size;
  17.     }
  18. };
  19.  
  20. int main(int argc, char *argv[])
  21. {
  22.     std::vector<foo_t *> vec;
  23.  
  24.     for (int i = 0; i < 17; i++)
  25.     {
  26.         foo_t *= new(std::nothrow) foo_t();
  27.         if (NULL == x)
  28.         {
  29.             goto fail;
  30.         }
  31.         else
  32.         {
  33.             x->size = 1;
  34.         }
  35.         vec.push_back(x);
  36.     }
  37.  
  38.     std::sort(vec.begin(), vec.end(), cmp_t());
  39. fail:
  40.     for(std::vector<foo_t *>::iterator iter = vec.begin(); vec.end() != iter; ++iter)
  41.     {
  42.         delete *iter;
  43.         *iter = NULL;
  44.     }
  45.  
  46.     return 0;
  47. }
然后编译
  1. g++ main.cpp -Werror -Wall -g
然后执行,此时系统出core,错误类型为段错误
如果无core文件产生,可以使用
  1. ulimit -c unlimited
后重新执行一次,此时就会有core文件生成
然后
  1. gdb a.out core
  2. (gdb) bt
  3. #0  0x0804889e in cmp_t::operator() (this=0xbfed92d0, a=0x0, b=0x9a9d0c8) at main.cpp:16
    #1  0x080497ff in std::__unguarded_partition<__gnu_cxx::__normal_iterator<foo_t**, std::vector<foo_t*,="" std::allocator > >, foo_t*, cmp_t> (__first=..., __last=..., __pivot=@0x9a9d1a0, __comp=...) at /usr/include/c++/4.6/bits/stl_algo.h:2233
    #2  0x0804926a in std::__unguarded_partition_pivot<__gnu_cxx::__normal_iterator<foo_t**, std::vector<foo_t*,="" std::allocator > >, cmp_t> (__first=..., __last=..., __comp=...) at /usr/include/c++/4.6/bits/stl_algo.h:2265
    #3  0x08048e84 in std::__introsort_loop<__gnu_cxx::__normal_iterator<foo_t**, std::vector<foo_t*,="" std::allocator > >, int, cmp_t> (
        __first=..., __last=..., __depth_limit=7, __comp=...) at /usr/include/c++/4.6/bits/stl_algo.h:2306
    #4  0x08048a22 in std::sort<__gnu_cxx::__normal_iterator<foo_t**, std::vector<foo_t*,="" std::allocator > >, cmp_t> (__first=..., 
        __last=..., __comp=...) at /usr/include/c++/4.6/bits/stl_algo.h:5368
    #5  0x080487ce in main (argc=1, argv=0xbfed9464) at main.cpp:38
     
可以看到,系统core在了排序函数里面。
然后通过分析stl代码发现以下一段代码
  1. /// This is a helper function...
  2.   template<typename _RandomAccessIterator, typename _Tp, typename _Compare>
  3.     _RandomAccessIterator
  4.     __unguarded_partition(_RandomAccessIterator __first,
  5.              _RandomAccessIterator __last,
  6.              const _Tp& __pivot, _Compare __comp)
  7.     {
  8.       while (true)
  9.     {
  10.      while (__comp(*__first, __pivot))
  11.      ++__first;
  12.      --__last;
  13.      while (__comp(__pivot, *__last))
  14.      --__last;
  15.      if (!(__first < __last))
  16.      return __first;
  17.      std::iter_swap(__first, __last);
  18.      ++__first;
  19.     }
  20.     }
此函数完成快速排序中分区功能,即将比哨兵小的数据放在其前,大的放在其后。
函数中使用的是
while (__comp(*__first, __pivot))
    ++__first;

如果当比较元素相同返回真时,此时比较元素将会继续向下遍历,在极端情况下,例如程序中所有元素都是一样的情况下,在这种情况下,就会出现访问越界,结果就是导致程序出现segment fault

所以在写c++ stl中的比较函数是,bool返回真的时候,一定是“真的”大,或者小,等于的时候只能返回false。


这个错误算是一次教训,所幸的是没有引起大范围的错误。

 

#include <iostream> //标准输入输出头文件 #include <pcl/point_types.h> //点类型定义头文件 #include <pcl/io/ply_io.h> //I/O操作头文件 #include <pcl/io/pcd_io.h> //I/O操作头文件 #include <pcl/visualization/pcl_visualizer.h> #include <pcl/sample_consensus/ransac.h> #include <pcl/sample_consensus/sac_model_line.h> #include <ctime> #include <pcl/filters/passthrough.h> #include <pcl/filters/voxel_grid.h> #include <pcl/filters/statistical_outlier_removal.h> #include <pcl/filters/radius_outlier_removal.h> #include <pcl/registration/icp.h> //ICP配准类相关头文件 #include <Eigen/src/Core/util/Macros.h> #include <pcl/pcl_macros.h> #include <pcl/common/transforms.h> #include <Eigen/Geometry> #include <pcl/kdtree/kdtree_flann.h> #include <pcl/features/normal_3d_omp.h> #include <boost/thread/thread.hpp> #include <algorithm> // 包含std::sort #include <pcl/surface/mls.h> #include <vector> #include <pcl/io/obj_io.h> #include <pcl/filters/convolution_3d.h> #include <pcl/filters/median_filter.h> #include <pcl/filters/random_sample.h> #include <pcl/point_cloud.h> #include <pcl/features/normal_3d.h> #include <pcl/features/fpfh.h> #include <pcl/registration/ia_ransac.h> #include <thread> #include <pcl/registration/ndt.h> #include <pcl/segmentation/extract_clusters.h> #include <pcl/kdtree/kdtree.h> #include <pcl/common/centroid.h> #include <pcl/common/common.h> #include <pcl/sample_consensus/sac_model_circle.h> #include <pcl/filters/extract_indices.h> #include <pcl/surface/convex_hull.h> #include <cmath> #include <memory> #include < pcl/sample_consensus/sac_model_circle3d.h> #include <pcl/segmentation/sac_segmentation.h> #include <pcl/common/geometry.h> #include <pcl/features/boundary.h> // 边界估计头文件 #include <pcl/filters/project_inliers.h> #include <Eigen/Dense> #include <pcl/sample_consensus/method_types.h> #include <pcl/sample_consensus/model_types.h> #include <pcl/visualization/cloud_viewer.h> #include <pcl/filters/crop_box.h> #include <pcl/search/kdtree.h> #include <string> #include <cstdlib> #include <pcl/registration/gicp.h> #include <pcl/console/time.h> #include <pcl/registration/icp_nl.h> // 注意:这里包含的是非线性ICP(使用LM)的头文件 #include <pcl/filters/approximate_voxel_grid.h> #include <omp.h> #include <chrono> #include <pcl/octree/octree_search.h> #include <pcl/features/impl/normal_3d_omp.hpp> #include "fast_gicp.hpp" #include <open3d/Open3D.h> #include <iomanip> // 添加这个头文件用于 std::setprecision #include <random> // 添加这个头文件用于随机数生成 using namespace std; typedef pcl::PointXYZ PointT; typedef pcl::PointCloud<PointT> PointCloudT; // /********************用于点面ICP配准****************//////////// //void cloudNormal(pcl::PointCloud<pcl::PointXYZ>::Ptr& cloud, pcl::PointCloud<pcl::PointNormal>::Ptr& cloud_normals) //{ // pcl::NormalEstimationOMP<pcl::PointXYZ, pcl::Normal> n;//OMP加速 // pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>); // pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>()); // n.setNumberOfThreads(8);//含义:设置OpenMP使用的线程数。这里设置为8个线程,用于加速法线估计计算。通常设置为可用CPU核心数。注意:如果系统不支持OpenMP或核心数不足,实际线程数可能受限。 // n.setInputCloud(cloud); // n.setSearchMethod(tree); // n.setKSearch(10); //设置法线估计时使用的最近邻点数量(k近邻搜索)。此方法指定每个点的法线基于其最近的k个邻居计算得到。 // n.compute(*normals); // pcl::concatenateFields(*cloud, *normals, *cloud_normals); // 拼接点云及法向量 //} // /********************创建带文字标签的自定义坐标轴****************//////////// void addCoordinateSystemWithLabels( pcl::visualization::PCLVisualizer::Ptr viewer, float axis_length, // 坐标轴长度 float line_width, // 线宽(粗细) float text_scale, // 文字大小 const std::string& id_prefix = "axis_", float origin_x = 0.0f, float origin_y = 0.0f, float origin_z = 0.0f ) { // X轴(红色) pcl::PointXYZ x_start(origin_x, origin_y, origin_z); pcl::PointXYZ x_end(origin_x + axis_length, origin_y, origin_z); viewer->addLine(x_start, x_end, 1.0, 0.0, 0.0, id_prefix + "x_line"); viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, line_width, id_prefix + "x_line"); // 添加X标签 viewer->addText3D("X", x_end, text_scale, 1.0, 0.0, 0.0, id_prefix + "x_label"); // Y轴(绿色) pcl::PointXYZ y_start(origin_x, origin_y, origin_z); pcl::PointXYZ y_end(origin_x, origin_y + axis_length, origin_z); viewer->addLine(y_start, y_end, 0.0, 1.0, 0.0, id_prefix + "y_line"); viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, line_width, id_prefix + "y_line"); // 添加Y标签 viewer->addText3D("Y", y_end, text_scale, 0.0, 1.0, 0.0, id_prefix + "y_label"); // Z轴(蓝色) pcl::PointXYZ z_start(origin_x, origin_y, origin_z); pcl::PointXYZ z_end(origin_x, origin_y, origin_z + axis_length); viewer->addLine(z_start, z_end, 0.0, 0.0, 1.0, id_prefix + "z_line"); viewer->setShapeRenderingProperties(pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, line_width, id_prefix + "z_line"); // 添加Z标签 viewer->addText3D("Z", z_end, text_scale, 0.0, 0.0, 1.0, id_prefix + "z_label"); } // 提前声明估算点云平均点间距的辅助函数 double estimatePointSpacing(const std::shared_ptr<open3d::geometry::PointCloud>& cloud, int num_samples = 1000); // 半径滤波函数 void radiusFilterDemo(const std::string& source_file, const std::string& target_file) { std::cout << "=== Open3D 半径滤波性能测试 ===" << std::endl; // 参数设置 const int min_neighbors = 40; // 最小邻居数 const double radius = 1.2; // 搜索半径 // 处理源点云 std::cout << "\n--- 处理源点云: " << source_file << " ---" << std::endl; auto start_load_source = std::chrono::high_resolution_clock::now(); auto source_cloud = open3d::io::CreatePointCloudFromFile(source_file); auto end_load_source = std::chrono::high_resolution_clock::now(); if (!source_cloud || source_cloud->points_.empty()) { std::cerr << "错误: 无法读取源点云文件 " << source_file << std::endl; return; } auto load_duration_source = std::chrono::duration_cast<std::chrono::milliseconds>( end_load_source - start_load_source); std::cout << "加载时间: " << load_duration_source.count() << " ms" << std::endl; std::cout << "滤波前点数: " << source_cloud->points_.size() << std::endl; // 执行半径滤波 auto start_filter_source = std::chrono::high_resolution_clock::now(); std::shared_ptr<open3d::geometry::PointCloud> filtered_source; std::vector<size_t> inlier_indices_source; std::tie(filtered_source, inlier_indices_source) = source_cloud->RemoveRadiusOutliers( min_neighbors, radius); auto end_filter_source = std::chrono::high_resolution_clock::now(); auto filter_duration_source = std::chrono::duration_cast<std::chrono::milliseconds>( end_filter_source - start_filter_source); std::cout << "滤波时间: " << filter_duration_source.count() << " ms" << std::endl; std::cout << "滤波后点数: " << filtered_source->points_.size() << std::endl; double retention_rate_source = (filtered_source->points_.size() * 100.0 / source_cloud->points_.size()); std::cout << "保留比例: " << std::fixed << std::setprecision(2) << retention_rate_source << "%" << std::endl; // 处理目标点云 std::cout << "\n--- 处理目标点云: " << target_file << " ---" << std::endl; auto start_load_target = std::chrono::high_resolution_clock::now(); auto target_cloud = open3d::io::CreatePointCloudFromFile(target_file); auto end_load_target = std::chrono::high_resolution_clock::now(); if (!target_cloud || target_cloud->points_.empty()) { std::cerr << "错误: 无法读取目标点云文件 " << target_file << std::endl; return; } auto load_duration_target = std::chrono::duration_cast<std::chrono::milliseconds>( end_load_target - start_load_target); std::cout << "加载时间: " << load_duration_target.count() << " ms" << std::endl; std::cout << "滤波前点数: " << target_cloud->points_.size() << std::endl; // 执行半径滤波 auto start_filter_target = std::chrono::high_resolution_clock::now(); std::shared_ptr<open3d::geometry::PointCloud> filtered_target; std::vector<size_t> inlier_indices_target; std::tie(filtered_target, inlier_indices_target) = target_cloud->RemoveRadiusOutliers( min_neighbors, radius); auto end_filter_target = std::chrono::high_resolution_clock::now(); auto filter_duration_target = std::chrono::duration_cast<std::chrono::milliseconds>( end_filter_target - start_filter_target); std::cout << "滤波时间: " << filter_duration_target.count() << " ms" << std::endl; std::cout << "滤波后点数: " << filtered_target->points_.size() << std::endl; double retention_rate_target = (filtered_target->points_.size() * 100.0 / target_cloud->points_.size()); std::cout << "保留比例: " << std::fixed << std::setprecision(2) << retention_rate_target << "%" << std::endl; // 保存滤波后的点云(可选) std::cout << "\n--- 保存结果 ---" << std::endl; if (open3d::io::WritePointCloud("filtered_source.pcd", *filtered_source)) { std::cout << "源点云滤波结果保存为: filtered_source.pcd" << std::endl; } if (open3d::io::WritePointCloud("filtered_target.pcd", *filtered_target)) { std::cout << "目标点云滤波结果保存为: filtered_target.pcd" << std::endl; } // 汇总统计 std::cout << "\n=== 性能汇总 ===" << std::endl; std::cout << "源点云 - 加载: " << load_duration_source.count() << " ms, " << "滤波: " << filter_duration_source.count() << " ms, " << "总处理: " << (load_duration_source.count() + filter_duration_source.count()) << " ms" << std::endl; std::cout << "目标点云 - 加载: " << load_duration_target.count() << " ms, " << "滤波: " << filter_duration_target.count() << " ms, " << "总处理: " << (load_duration_target.count() + filter_duration_target.count()) << " ms" << std::endl; } // 估算点云平均点间距的辅助函数 double estimatePointSpacing(const std::shared_ptr<open3d::geometry::PointCloud>& cloud, int num_samples) { if (cloud->points_.size() < 2) return 0.01; open3d::geometry::KDTreeFlann kdtree(*cloud); double total_distance = 0.0; int count = 0; // 随机采样计算平均最近邻距离 std::random_device rd; std::mt19937 gen(rd()); std::uniform_int_distribution<> dis(0, static_cast<int>(cloud->points_.size()) - 1); for (int i = 0; i < std::min(num_samples, static_cast<int>(cloud->points_.size())); ++i) { int idx = dis(gen); std::vector<int> indices; std::vector<double> distances; if (kdtree.SearchKNN(cloud->points_[idx], 2, indices, distances) == 2) { total_distance += std::sqrt(distances[1]); // 第一个是自身 count++; } } return count > 0 ? total_distance / count : 0.01; } // 自适应参数版本(可选) void adaptiveRadiusFilterDemo(const std::string& source_file, const std::string& target_file) { std::cout << "\n=== 自适应参数半径滤波 ===" << std::endl; // 处理源点云 auto source_cloud = open3d::io::CreatePointCloudFromFile(source_file); if (!source_cloud || source_cloud->points_.empty()) return; // 估算点云密度并自适应参数 double avg_spacing = estimatePointSpacing(source_cloud); double adaptive_radius = avg_spacing * 4.0; // 半径为平均点间距的4倍 int adaptive_min_neighbors = static_cast<int>(15 * (0.01 / avg_spacing)); // 基于密度调整 std::cout << "估算点间距: " << avg_spacing << " m" << std::endl; std::cout << "自适应参数 - 半径: " << adaptive_radius << ", 最小邻居: " << adaptive_min_neighbors << std::endl; auto start_time = std::chrono::high_resolution_clock::now(); auto filtered_result = source_cloud->RemoveRadiusOutliers(adaptive_min_neighbors, adaptive_radius); auto end_time = std::chrono::high_resolution_clock::now(); auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time); auto filtered_source = std::get<0>(filtered_result); std::cout << "自适应滤波时间: " << duration.count() << " ms" << std::endl; std::cout << "滤波前: " << source_cloud->points_.size() << " 点" << std::endl; std::cout << "滤波后: " << filtered_source->points_.size() << " 点" << std::endl; } int main(int argc, char** argv) { pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud2(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud3(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud4(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud5(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud6(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered1(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered2(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered3(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered4(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered5(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered6(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered7(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered8(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered9(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered10(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered11(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered12(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered13(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered14(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered15(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered16(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered17(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered18(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered19(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered20(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered21(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered22(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered23(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered24(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered25(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered26(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered27(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered28(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered29(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr clouda(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloudb(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloudc(new pcl::PointCloud<pcl::PointXYZ>); //创建两个pcl::PointCloud<pcl::PointXYZ>共享指针,并初始化它们 pcl::PointCloud<pcl::PointXYZ>::Ptr clouda_left(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloudb_left(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloudb_right(new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr clouda_right(new pcl::PointCloud<pcl::PointXYZ>); //if (pcl::io::loadPCDFile("point_cloud_20251119.pcd", *clouda) < 0)//读取标准点云 //{ // std::cout << "error"; //} //std::cout << "点云数量: " << clouda->points.size() << std::endl; clock_t start34 = clock(); if (pcl::io::loadPCDFile("point_cloud_2025119_20_10.pcd", *cloudb) < 0) //读取采集点云 { std::cout << "error"; } std::cout << "点云数量: " << cloudb->points.size() << std::endl; clock_t end34 = clock(); // 记录程序运行到此结束时间 double cpu_time_used34 = static_cast<double>(end34 - start34) / CLOCKS_PER_SEC; //CLOCKS_PER_SEC将结果转为以秒为单位 std::cout << "PCL读取点云文件时间: " << cpu_time_used34 << " s\n"; //显示此段程序运行时间 clock_t start33 = clock(); pcl::RadiusOutlierRemoval<pcl::PointXYZ> ror1; // 半径滤波 ror1.setInputCloud(cloudb); //输入点云 ror1.setRadiusSearch(1.2f); // 设置搜索半径 ror1.setMinNeighborsInRadius(40); // 设置半径范围内的最少点数阈值 ror1.filter(*cloudb); //保存滤波结果 std::cout << "3号相机采集--分割的ZDB点云数量--cloudb_left去除离散点后 " << cloudb->points.size() << std::endl; clock_t end33 = clock(); // 记录程序运行到此结束时间 double cpu_time_used33 = static_cast<double>(end33 - start33) / CLOCKS_PER_SEC; //CLOCKS_PER_SEC将结果转为以秒为单位 std::cout << "PCL采集离散点滤波时间: " << cpu_time_used33 << " s\n"; //显示此段程序运行时间 // 设置点云文件路径 std::string source_file = "point_cloud_2025119_20_10.pcd"; std::string target_file = "point_cloud_2025119_20_10.pcd"; // 执行标准半径滤波 radiusFilterDemo(source_file, target_file); // 可选:执行自适应参数版本 // adaptiveRadiusFilterDemo(source_file, target_file); std::cout << "\n=== 处理完成 ===" << std::endl; if (pcl::io::loadPCDFile("filtered_source.pcd", *cloudc) < 0) //读取采集点云 { std::cout << "error"; } std::cout << "点云数量: " << cloudc->points.size() << std::endl; ////................单个窗口显示两个图像.............// // 1. 创建PCLVisualizer对象 pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer")); viewer->setBackgroundColor(0, 0, 0); // 设置背景颜色为黑色 // 添加带文字标签的坐标轴 //参数:viewer, 轴长度, 线宽, 文字大小, 标识前缀, 原点坐标 addCoordinateSystemWithLabels(viewer,800.0, 2.0, 50.0, "main_axis_", 0, 0, 0); //pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> clouda_color(clouda_left, 0, 255, 0); // 设置为绿色 //viewer->addPointCloud<pcl::PointXYZ>(clouda_left, clouda_color, "clouda"); //viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "clouda"); // 设置点大小 pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> clouda_color1(cloudc, 0, 255, 0); // 设置为绿色 viewer->addPointCloud<pcl::PointXYZ>(cloudc, clouda_color1, "cloudb_left"); viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "clouda"); // 设置点大小 // 5. 将第二个点云添加到可视化窗口并设置颜色(红色) pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud2_color(cloudb, 255, 0, 255); // 设置为粉色 viewer->addPointCloud<pcl::PointXYZ>(cloudb, cloud2_color, "cloud2"); viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "clouda"); // 设置点大小 //pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> cloud3_color(cloudb_right, 255, 0, 255); // 设置为红色 //viewer->addPointCloud<pcl::PointXYZ>(cloudb_right, cloud3_color, "cloud3"); //viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "cloud3"); // 设置点大小 // 6. 启动可视化 while (!viewer->wasStopped()) { viewer->spinOnce(100); // 让可视化窗口刷新 } 上述程序中,实现了PCL的半径滤波和Open3D的半径滤波时间对比,但是存在一个问题,原来程序中没有Open3D的半径滤波,PCL读取文件时间约为0.5s,后来添加了Open3D的半径滤波,导致PCL读取文件时间变为5s左右,时间明显变长,原因?开发环境,VS2022 PCL1.14.1版 Open3D 0.19版
最新发布
11-23
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