我正在使用PCL处理点云，以便于检测场景中的物体。我添加了一个自定义的PointT类型，并且它能正常工作。然而，我在PCL库中的滤波算法方面遇到了困难。我尝试了统计、半径和条件异常值去除来消除噪声。统计学算法没有返回任何结果(它似乎陷入了无限循环)，而半径则返回大小为0的云。条件算法实际上返回相同的云，没有移除任何点。在半径和统计算法中，我按照给定的示例进行操作，但它们都没有起作用。
目前，我认为条件去除算法对我来说是最合适的算法，因为我想要删除置信度不在[0.4-1]范围内的任何点。正如我之前提到的，我正在使用自定义点类型。以下是Point Type（Tango3DPoitType）的代码以及使用条件去除的方法。
目前，我认为条件去除算法对我来说是最合适的算法，因为我想要删除置信度不在[0.4-1]范围内的任何点。正如我之前提到的，我正在使用自定义点类型。以下是Point Type（Tango3DPoitType）的代码以及使用条件去除的方法。
#define PCL_NO_PRECOMPILE
#include <pcl/point_types.h>
#include <pcl/impl/point_types.hpp>
#include <pcl/point_cloud.h>
#include <pcl/impl/instantiate.hpp>
// Preserve API for PCL users < 1.4
#include <pcl/common/distances.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/kdtree/impl/kdtree_flann.hpp>
#include <pcl/search/organized.h>
#include <pcl/search/impl/organized.hpp>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/filters/impl/statistical_outlier_removal.hpp>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/filters/impl/radius_outlier_removal.hpp>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/impl/voxel_grid.hpp>
#include <pcl/filters/voxel_grid_covariance.h>
#include <pcl/filters/impl/voxel_grid_covariance.hpp>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/impl/extract_indices.hpp>
#include <pcl/filters/conditional_removal.h>
#include <pcl/filters/impl/conditional_removal.hpp>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/impl/sac_segmentation.hpp>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/segmentation/impl/extract_clusters.hpp>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
struct EIGEN_ALIGN16 _Tango3DPoitType
{
PCL_ADD_POINT4D; // This adds the members x,y,z which can also be accessed using the point (which is float[4])
union
{
union
{
struct
{
uint8_t b;
uint8_t g;
uint8_t r;
uint8_t a;
}; float rgb;
}; uint32_t rgba;
};
float Confidence;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW };
struct EIGEN_ALIGN16 Tango3DPoitType : public _Tango3DPoitType
{
inline Tango3DPoitType ()
{
x = y = z = 0.0f;
data[3] = 1.0f;
r = b = a = 0;
g = 255;
Confidence = 0.0f;
}
inline Tango3DPoitType (float _Confidence)
{
x = y = z = 0.0f;
data[3] = 1.0f;
r = b = a = 0;
g = 255;
Confidence = _Confidence;
}
inline Tango3DPoitType (uint8_t _r, uint8_t _g, uint8_t _b)
{
x = y = z = 0.0f;
data[3] = 1.0f;
r = _r;
g = _g;
b = _b;
a = 0;
Confidence = 0;
}
inline Eigen::Vector3i getRGBVector3i () { return (Eigen::Vector3i (r, g, b)); }
inline const Eigen::Vector3i getRGBVector3i () const { return (Eigen::Vector3i (r, g, b)); }
inline Eigen::Vector4i getRGBVector4i () { return (Eigen::Vector4i (r, g, b, 0)); }
inline const Eigen::Vector4i getRGBVector4i () const { return (Eigen::Vector4i (r, g, b, 0)); }
EIGEN_MAKE_ALIGNED_OPERATOR_NEW };
// Adding confidence as fourth data to XYZ
POINT_CLOUD_REGISTER_POINT_STRUCT (Tango3DPoitType,
(float, x, x)
(float, y, y)
(float, z, z)
(uint32_t, rgba, rgba)
(float, Confidence, Confidence)
)
POINT_CLOUD_REGISTER_POINT_WRAPPER(Tango3DPoitType, _Tango3DPoitType)
条件移除方法
void CloudDenoising(const pcl::PointCloud<Tango3DPoitType>::Ptr source,
const pcl::PointCloud<Tango3DPoitType>::Ptr target){
// build the condition
pcl::ConditionAnd<Tango3DPoitType>::Ptr ConfidenceRangeCondition (new pcl::ConditionAnd<Tango3DPoitType> ());
ConfidenceRangeCondition->addComparison (pcl::FieldComparison<Tango3DPoitType>::ConstPtr (new pcl::FieldComparison<Tango3DPoitType> ("Confidence", pcl::ComparisonOps::GT, 0.5)));
ConfidenceRangeCondition->addComparison (pcl::FieldComparison<Tango3DPoitType>::ConstPtr (new pcl::FieldComparison<Tango3DPoitType> ("Confidence", pcl::ComparisonOps::LT, 1.1)));
// build the filter
pcl::ConditionalRemoval<Tango3DPoitType> conditionalRemoval;
conditionalRemoval.setCondition (ConfidenceRangeCondition);
conditionalRemoval.setInputCloud (source);
conditionalRemoval.setKeepOrganized(true);
// apply filter
conditionalRemoval.filter (*target);
}
我想了解的是，是我的点类型有问题还是PCL库中存在bug。
谢谢。