我使用libLAS库来读取.las file的云点。然后,我将点存储在PCL点云变量中,以便使用点云库处理和显示点云。
这是我使用的代码:
class PointCloud
{
public:
//PointCloud(const std::string& path);
uint32_t getVertsCount();
float4* getVertsData();
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr read(const std::string& path);//void read(const std::string &path);
}
template<typename PointT>
typename pcl::PointCloud<PointT>::Ptr PointCloud::read(const string& path)
{
typename pcl::PointCloud<PointT>::Ptr lasCloud(new pcl::PointCloud<PointT>);
std::ifstream ifs;
ifs.open(path, std::ios::in | std::ios::binary);
//std::ifstream inf(path, std::ios::in | std::ios::binary);
liblas::ReaderFactory f;
liblas::Reader reader = f.CreateWithStream(ifs);
liblas::Header const& header = reader.GetHeader();
std::cout << "Compressed: " << (header.Compressed() == true) ? "true" : "false";
std::cout << "Signature: " << header.GetFileSignature() << '\n';
std::cout << "Points count: " << header.GetPointRecordsCount() << '\n';
while (reader.ReadNextPoint())
{
liblas::Point const& p = reader.GetPoint();
PointT cloudPoint;
cloudPoint.x = float(p.GetX()) * 0.001 + 590284.000; // (double)(x * scaleX) + offsetX;
cloudPoint.y = float(p.GetY()) * 0.001 + 4339456.000; // (double)(y * scaleY) + offsetY;
cloudPoint.z = float(p.GetZ()) * 0.001 + 157.000; // (double)(z * scaleZ) + offsetZ;
std::cout << p.GetX() << ", " << p.GetY() << ", " << p.GetZ() << "\n";
//cloudPoint.intensity = p.GetIntensity(); // (double)(intensity) / 65536.0;
lasCloud->points.push_back(cloudPoint);
}
if (!ifs.good())
throw runtime_error("Reading went wrong!");
lasCloud->width = lasCloud->points.size();
lasCloud->height = 1;
lasCloud->is_dense = true;
std::cout << "Cloud size = " << lasCloud->points.size() << endl;
return lasCloud;
}
int main (int argc, char** argv)
{
std::cout << "starting enviroment" << std::endl;
pcl::visualization::PCLVisualizer::Ptr viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
CameraAngle setAngle = FPS; //XY, FPS, Side, TopDown
initCamera(setAngle, viewer);
pcl::PointCloud<pcl::PointXYZ>::Ptr inputCloudI; //
inputCloudI = pcd.read<pcl::PointXYZ>("C:/Users/hedey/OneDrive/Documents/Research_papers/STDF/10_4231_MFQF-Q141/I-65/LiDAR/RoadSurface/NB/20180524_I65_NB_RoadSurface_1_50.5.las");
std::cout << "Cloud size = " << inputCloudI->points.size() << endl;
renderPointCloud(viewer, inputCloudI, "lasCloud");
while (!viewer->wasStopped())
{
viewer->spinOnce();
}
}但是,使用PCL查看器显示的云看起来像一个点。我注意到的是,当我打印出使用libLAS读取的坐标时,x&y坐标没有十进制值,这与las文件中存储的实际坐标相比是不准确的。我在命令提示符下使用las2txt获得了实际的点坐标。包含实际坐标的This is the txt file。下面是显示cout结果的图像:

此外,这也是我使用CloudCompare打开点云时的外观。我期待着在将其读取到PCL点云变量中并使用PCL查看器显示结果时显示相同的结果,因为我需要做进一步的处理才能进行传感器融合(相机和激光雷达)。

发布于 2021-02-04 05:46:59
std::cout的默认精度为6位十进制数字。请添加如下内容
std::cout.precision(12);在while循环之前。
此外,将p.GetX()等转换为float是没有意义的:如果将其与0.001相乘,运算符*的参数左侧自然会被提升为至少double。然而,float只有大约7位的精度,所以对于存储在双精度中的9位整数(是的!)这样的截断是灾难性的。
还有另一个(小)错误,正确的行为
std::cout << "Compressed: " << ((header.Compressed() == true) ? "true\n" : "false\n");请注意条件表达式(和\n)周围的()大括号。请使用标准的编译器选项,这些选项将对这些简单的问题发出警告。
请同时阅读https://stackoverflow.com/help/minimal-reproducible-example
https://stackoverflow.com/questions/66035067
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