基于PCL- 利用kdtree计算点云点距均值

//功能：利用kdtree计算点云点距均值
//cloud：代表输入点云
//k：代表定查询点邻域周围最近的K个点
float computeCloudResolution(const pcl::PointCloud<pcl::PointNormal>::ConstPtr &cloud, int k)
{
	double res = 0.0;
	int n_points = 0;
	pcl::KdTreeFLANN<pcl::PointNormal> tree;
	tree.setInputCloud(cloud);
	for (size_t i = 0; i < cloud->size(); ++i)
	{
		if (!pcl_isfinite((*cloud)[i].x))    //pcl_isfinite函数返回一个布尔值，检查某个值是不是正常数值
		{
			continue;
		}
		std::vector<int> indices(k);         //创建一个动态数组，存储查询点近邻索引
		std::vector<float> sqr_distances(k); //存储近邻点对应平方距离
		if (tree.nearestKSearch(i, k, indices, sqr_distances) == k)
		{
			for (int i = 1; i < k; i++)
			{
				res += sqrt(sqr_distances[i]);
				++n_points;
			}
		}
	}
	if (n_points != 0)
	{
		res /= n_points;
	}
	return res;
}

标签：distances,pcl,kdtree,int,res,points,点云点,PCL,cloud
来源： https://blog.csdn.net/qq_41188371/article/details/113831139