Point Cloud Library (PCL)  1.14.1-dev
segment_differences.h
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37 
38 #pragma once
39 
40 #include <pcl/pcl_base.h>
41 #include <pcl/pcl_macros.h>
42 #include <pcl/search/search.h> // for Search
43 
44 namespace pcl
45 {
46  ////////////////////////////////////////////////////////////////////////////////////////////
47  /** \brief Obtain the difference between two aligned point clouds as another point cloud, given a distance threshold.
48  * \param src the input point cloud source
49  * \param threshold the distance threshold (tolerance) for point correspondences. (e.g., check if f a point p1 from
50  * src has a correspondence > threshold than a point p2 from tgt)
51  * \param tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching built over the target cloud
52  * \param output the resultant output point cloud difference
53  * \ingroup segmentation
54  */
55  template <typename PointT>
57  const pcl::PointCloud<PointT> &src,
58  double threshold,
59  const typename pcl::search::Search<PointT>::Ptr &tree,
60  pcl::PointCloud<PointT> &output);
61 
62  ////////////////////////////////////////////////////////////////////////////////////////////
63  ////////////////////////////////////////////////////////////////////////////////////////////
64  ////////////////////////////////////////////////////////////////////////////////////////////
65  /** \brief @b SegmentDifferences obtains the difference between two spatially
66  * aligned point clouds and returns the difference between them for a maximum
67  * given distance threshold.
68  * \author Radu Bogdan Rusu
69  * \ingroup segmentation
70  */
71  template <typename PointT>
72  class SegmentDifferences: public PCLBase<PointT>
73  {
75 
76  public:
78  using PointCloudPtr = typename PointCloud::Ptr;
80 
82  using KdTreePtr = typename KdTree::Ptr;
83 
86 
87  /** \brief Empty constructor. */
88  SegmentDifferences () = default;
89 
90  /** \brief Provide a pointer to the target dataset against which we
91  * compare the input cloud given in setInputCloud
92  *
93  * \param cloud the target PointCloud dataset
94  */
95  inline void
96  setTargetCloud (const PointCloudConstPtr &cloud) { target_ = cloud; }
97 
98  /** \brief Get a pointer to the input target point cloud dataset. */
99  inline PointCloudConstPtr const
100  getTargetCloud () { return (target_); }
101 
102  /** \brief Provide a pointer to the search object.
103  * \param tree a pointer to the spatial search object.
104  */
105  inline void
106  setSearchMethod (const KdTreePtr &tree) { tree_ = tree; }
107 
108  /** \brief Get a pointer to the search method used. */
109  inline KdTreePtr
110  getSearchMethod () { return (tree_); }
111 
112  /** \brief Set the maximum distance tolerance (squared) between corresponding
113  * points in the two input datasets.
114  *
115  * \param sqr_threshold the squared distance tolerance as a measure in L2 Euclidean space
116  */
117  inline void
118  setDistanceThreshold (double sqr_threshold) { distance_threshold_ = sqr_threshold; }
119 
120  /** \brief Get the squared distance tolerance between corresponding points as a
121  * measure in the L2 Euclidean space.
122  */
123  inline double
125 
126  /** \brief Segment differences between two input point clouds.
127  * \param output the resultant difference between the two point clouds as a PointCloud
128  */
129  void
130  segment (PointCloud &output);
131 
132  protected:
133  // Members derived from the base class
134  using BasePCLBase::input_;
135  using BasePCLBase::indices_;
138 
139  /** \brief A pointer to the spatial search object. */
140  KdTreePtr tree_{nullptr};
141 
142  /** \brief The input target point cloud dataset. */
144 
145  /** \brief The distance tolerance (squared) as a measure in the L2
146  * Euclidean space between corresponding points.
147  */
148  double distance_threshold_{0.0};
149 
150  /** \brief Class getName method. */
151  virtual std::string
152  getClassName () const { return ("SegmentDifferences"); }
153  };
154 }
155 
156 #ifdef PCL_NO_PRECOMPILE
157 #include <pcl/segmentation/impl/segment_differences.hpp>
158 #endif
PCL base class.
Definition: pcl_base.h:70
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:147
typename PointCloud::Ptr PointCloudPtr
Definition: pcl_base.h:73
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: pcl_base.h:74
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition: pcl_base.h:150
bool initCompute()
This method should get called before starting the actual computation.
Definition: pcl_base.hpp:138
PointIndices::ConstPtr PointIndicesConstPtr
Definition: pcl_base.h:77
bool deinitCompute()
This method should get called after finishing the actual computation.
Definition: pcl_base.hpp:175
PointIndices::Ptr PointIndicesPtr
Definition: pcl_base.h:76
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:413
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:414
SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the ...
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
void setTargetCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the target dataset against which we compare the input cloud given in setInputClo...
double getDistanceThreshold()
Get the squared distance tolerance between corresponding points as a measure in the L2 Euclidean spac...
void segment(PointCloud &output)
Segment differences between two input point clouds.
KdTreePtr tree_
A pointer to the spatial search object.
PointCloudConstPtr target_
The input target point cloud dataset.
PointCloudConstPtr const getTargetCloud()
Get a pointer to the input target point cloud dataset.
KdTreePtr getSearchMethod()
Get a pointer to the search method used.
typename KdTree::Ptr KdTreePtr
void setDistanceThreshold(double sqr_threshold)
Set the maximum distance tolerance (squared) between corresponding points in the two input datasets.
double distance_threshold_
The distance tolerance (squared) as a measure in the L2 Euclidean space between corresponding points.
virtual std::string getClassName() const
Class getName method.
SegmentDifferences()=default
Empty constructor.
Generic search class.
Definition: search.h:75
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition: search.h:81
void getPointCloudDifference(const pcl::PointCloud< PointT > &src, double threshold, const typename pcl::search::Search< PointT >::Ptr &tree, pcl::PointCloud< PointT > &output)
Obtain the difference between two aligned point clouds as another point cloud, given a distance thres...
Defines all the PCL and non-PCL macros used.
shared_ptr< ::pcl::PointIndices > Ptr
Definition: PointIndices.h:13
shared_ptr< const ::pcl::PointIndices > ConstPtr
Definition: PointIndices.h:14