PCL学习：深度图像-从点云创建深度图像

此程序是生成一个矩形的点云，然后基于该点云创建深度图像 ，并显示点云和深度图；

#include 
#include 
#include 
#include 

void
setViewerPose(pcl::visualization::PCLVisualizer& viewer, const Eigen::Affine3f& viewer_pose)
{
	Eigen::Vector3f pos_vector = viewer_pose * Eigen::Vector3f(0, 0, 0);
	Eigen::Vector3f look_at_vector = viewer_pose.rotation() * Eigen::Vector3f(0, 0, 1) + pos_vector;
	Eigen::Vector3f up_vector = viewer_pose.rotation() * Eigen::Vector3f(0, -1, 0);
	viewer.setCameraPosition(
		pos_vector[0], 
		pos_vector[1], 
		pos_vector[2],
		look_at_vector[0], 
		look_at_vector[1], 
		look_at_vector[2],
		up_vector[0], 
		up_vector[1], 
		up_vector[2]);
}

int 
main (int argc, char** argv) {
	pcl::PointCloud pointCloud;
	//生成数据
	for (float y=-0.5f; y<=0.5f; y+=0.01f) 
	{
		for (float z=-0.5f; z<=0.5f; z+=0.01f) 
		{
			pcl::PointXYZ point;
			point.x = 2.0f - y;
			point.y = y;
			point.z = z;
			pointCloud.points.push_back(point);
		}
	}
	pointCloud.width = (uint32_t) pointCloud.points.size();
	pointCloud.height = 1;
	/*以1度为角分辨率，从上面创建的点云创建深度图像。*/
	float angularResolution = (float) (  1.0f * (M_PI/180.0f));  // 1度转弧度
	/*模拟深度传感器在水平方向的最大采样角度:360°视角。*/
	float maxAngleWidth     = (float) (360.0f * (M_PI/180.0f));  // 360.0度转弧度
	/*模拟深度传感器在垂直方向的最大采样角度:180°视角。*/
	float maxAngleHeight    = (float) (180.0f * (M_PI/180.0f)); // 180.0度转弧度
	/*
	sensorPose定义模拟深度传感器的6自由度位置：
	横滚角roll、俯仰角pitch、偏航角yaw，默认初始值均为0。
	*/
	Eigen::Affine3f sensorPose = (Eigen::Affine3f)Eigen::Translation3f(0.0f, 0.0f, 0.0f);
	/*
	定义坐标轴正方向：
	CAMERA_FRAME：X轴向右，Y轴向下，Z轴向前；
	LASER_FRAME：X轴向前，Y轴向左，Z轴向上；
	*/
	pcl::RangeImage::CoordinateFrame coordinate_frame = pcl::RangeImage::CAMERA_FRAME;
	/*
	noiseLevel设置周围点对当前点深度值的影响：
	如 noiseLevel=0.05，可以理解为，深度距离值是通过查询点半径为 Scm 的圆内包含的点用来平均计算而得到的。
	*/
	float noiseLevel=0.00;
	/*
	min_range设置最小的获取距离，小于最小获取距离的位置为盲区
	*/
	float minRange = 0.0f;
	/*border_size获得深度图像的边缘的宽度：在裁剪图像时，如果 borderSize>O ，将在图像周围留下当前视点不可见点的边界*/
	int borderSize = 1;

	boost::shared_ptr range_image_ptr(new pcl::RangeImage);
	pcl::RangeImage& range_image = *range_image_ptr;
	range_image.createFromPointCloud(pointCloud, angularResolution, maxAngleWidth, maxAngleHeight, sensorPose, coordinate_frame, noiseLevel, minRange, borderSize);
	std::cout << range_image << "\n";

	//创建3D视图并且添加点云进行显示
	pcl::visualization::PCLVisualizer viewer("3D Viewer");
	viewer.setBackgroundColor(1, 1, 1);
	pcl::visualization::PointCloudColorHandlerCustom range_image_color_handler(range_image_ptr, 0, 0, 0);
	viewer.addPointCloud(range_image_ptr, range_image_color_handler, "range image");
	viewer.setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "range image");

	viewer.initCameraParameters();
	setViewerPose(viewer, range_image.getTransformationToWorldSystem());
	
	//显示深度图像
	pcl::visualization::RangeImageVisualizer range_image_widget("Range image");
	//range_image_widget.setRangeImage(range_image);           
	range_image_widget.showRangeImage(range_image); //图像可视化方式显示深度图像
	Eigen::Affine3f scene_sensor_pose(Eigen::Affine3f::Identity());
	//主循环
	while (!viewer.wasStopped())
	{
		range_image_widget.spinOnce();
		viewer.spinOnce();
		pcl_sleep(0.01);
		if (1)
		{
			scene_sensor_pose = viewer.getViewerPose();
			range_image.createFromPointCloud(pointCloud, 
				angularResolution, pcl::deg2rad(360.0f), 
				pcl::deg2rad(180.0f), 
				scene_sensor_pose, 
				pcl::RangeImage::LASER_FRAME, 
				noiseLevel, 
				minRange, 
				borderSize);
			range_image_widget.showRangeImage(range_image);
		}
	}

}