Point Cloud Library (PCL)  1.12.1-dev
transformation_validation_euclidean.h
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40 
41 #pragma once
42 
43 #include <pcl/kdtree/kdtree.h>
44 #include <pcl/registration/transformation_validation.h>
45 #include <pcl/search/kdtree.h>
46 #include <pcl/memory.h>
47 #include <pcl/pcl_macros.h>
48 #include <pcl/point_representation.h>
49 
50 namespace pcl {
51 namespace registration {
52 /** \brief TransformationValidationEuclidean computes an L2SQR norm between a source and
53  * target dataset.
54  *
55  * To prevent points with bad correspondences to contribute to the overall score, the
56  * class also accepts a maximum_range parameter given via \ref setMaxRange that is used
57  * as a cutoff value for nearest neighbor distance comparisons.
58  *
59  * The output score is normalized with respect to the number of valid correspondences
60  * found.
61  *
62  * Usage example:
63  * \code
64  * pcl::TransformationValidationEuclidean<pcl::PointXYZ, pcl::PointXYZ> tve;
65  * tve.setMaxRange (0.01); // 1cm
66  * double score = tve.validateTransformation (source, target, transformation);
67  * \endcode
68  *
69  * \note The class is templated on the source and target point types as well as on the
70  * output scalar of the transformation matrix (i.e., float or double). Default: float.
71  * \author Radu B. Rusu
72  * \ingroup registration
73  */
74 template <typename PointSource, typename PointTarget, typename Scalar = float>
76 public:
77  using Matrix4 =
79 
80  using Ptr = shared_ptr<TransformationValidation<PointSource, PointTarget, Scalar>>;
81  using ConstPtr =
82  shared_ptr<const TransformationValidation<PointSource, PointTarget, Scalar>>;
83 
85  using KdTreePtr = typename KdTree::Ptr;
86 
88 
90  typename TransformationValidation<PointSource,
91  PointTarget>::PointCloudSourceConstPtr;
93  typename TransformationValidation<PointSource,
94  PointTarget>::PointCloudTargetConstPtr;
95 
96  /** \brief Constructor.
97  * Sets the \a max_range parameter to double::max, \a threshold_ to NaN
98  * and initializes the internal search \a tree to a FLANN kd-tree.
99  */
101  : max_range_(std::numeric_limits<double>::max())
102  , threshold_(std::numeric_limits<double>::quiet_NaN())
103  , tree_(new pcl::search::KdTree<PointTarget>)
104  , force_no_recompute_(false)
105  {}
106 
108 
109  /** \brief Set the maximum allowable distance between a point and its correspondence
110  * in the target in order for a correspondence to be considered \a valid. Default:
111  * double::max. \param[in] max_range the new maximum allowable distance
112  */
113  inline void
114  setMaxRange(double max_range)
115  {
116  max_range_ = max_range;
117  }
118 
119  /** \brief Get the maximum allowable distance between a point and its
120  * correspondence, as set by the user.
121  */
122  inline double
124  {
125  return (max_range_);
126  }
127 
128  /** \brief Provide a pointer to the search object used to find correspondences in
129  * the target cloud.
130  * \param[in] tree a pointer to the spatial search object.
131  * \param[in] force_no_recompute If set to true, this tree will NEVER be
132  * recomputed, regardless of calls to setInputTarget. Only use if you are
133  * confident that the tree will be set correctly.
134  */
135  inline void
136  setSearchMethodTarget(const KdTreePtr& tree, bool force_no_recompute = false)
137  {
138  tree_ = tree;
139  force_no_recompute_ = force_no_recompute;
140  }
141 
142  /** \brief Set a threshold for which a specific transformation is considered valid.
143  *
144  * \note Since we're using MSE (Mean Squared Error) as a metric, the threshold
145  * represents the mean Euclidean distance threshold over all nearest neighbors
146  * up to max_range.
147  *
148  * \param[in] threshold the threshold for which a transformation is vali
149  */
150  inline void
151  setThreshold(double threshold)
152  {
153  threshold_ = threshold;
154  }
155 
156  /** \brief Get the threshold for which a specific transformation is valid. */
157  inline double
159  {
160  return (threshold_);
161  }
162 
163  /** \brief Validate the given transformation with respect to the input cloud data, and
164  * return a score.
165  *
166  * \param[in] cloud_src the source point cloud dataset
167  * \param[in] cloud_tgt the target point cloud dataset
168  * \param[out] transformation_matrix the resultant transformation matrix
169  *
170  * \return the score or confidence measure for the given
171  * transformation_matrix with respect to the input data
172  */
173  double
175  const PointCloudTargetConstPtr& cloud_tgt,
176  const Matrix4& transformation_matrix) const;
177 
178  /** \brief Comparator function for deciding which score is better after running the
179  * validation on multiple transforms.
180  *
181  * \param[in] score1 the first value
182  * \param[in] score2 the second value
183  *
184  * \return true if score1 is better than score2
185  */
186  virtual bool
187  operator()(const double& score1, const double& score2) const
188  {
189  return (score1 < score2);
190  }
191 
192  /** \brief Check if the score is valid for a specific transformation.
193  *
194  * \param[in] cloud_src the source point cloud dataset
195  * \param[in] cloud_tgt the target point cloud dataset
196  * \param[out] transformation_matrix the transformation matrix
197  *
198  * \return true if the transformation is valid, false otherwise.
199  */
200  virtual bool
202  const PointCloudTargetConstPtr& cloud_tgt,
203  const Matrix4& transformation_matrix) const
204  {
205  if (std::isnan(threshold_)) {
206  PCL_ERROR("[pcl::TransformationValidationEuclidean::isValid] Threshold not set! "
207  "Please use setThreshold () before continuing.\n");
208  return (false);
209  }
210 
211  return (validateTransformation(cloud_src, cloud_tgt, transformation_matrix) <
212  threshold_);
213  }
214 
215 protected:
216  /** \brief The maximum allowable distance between a point and its correspondence in
217  * the target in order for a correspondence to be considered \a valid. Default:
218  * double::max.
219  */
220  double max_range_;
221 
222  /** \brief The threshold for which a specific transformation is valid.
223  * Set to NaN by default, as we must require the user to set it.
224  */
225  double threshold_;
226 
227  /** \brief A pointer to the spatial search object. */
229 
230  /** \brief A flag which, if set, means the tree operating on the target cloud
231  * will never be recomputed*/
233 
234  /** \brief Internal point representation uses only 3D coordinates for L2 */
235  class MyPointRepresentation : public pcl::PointRepresentation<PointTarget> {
238 
239  public:
240  using Ptr = shared_ptr<MyPointRepresentation>;
241  using ConstPtr = shared_ptr<const MyPointRepresentation>;
242 
244  {
245  nr_dimensions_ = 3;
246  trivial_ = true;
247  }
248 
249  /** \brief Empty destructor */
250  virtual ~MyPointRepresentation() = default;
251 
252  virtual void
253  copyToFloatArray(const PointTarget& p, float* out) const
254  {
255  out[0] = p.x;
256  out[1] = p.y;
257  out[2] = p.z;
258  }
259  };
260 
261 public:
263 };
264 } // namespace registration
265 } // namespace pcl
266 
267 #include <pcl/registration/impl/transformation_validation_euclidean.hpp>
PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensi...
int nr_dimensions_
The number of dimensions in this point's vector (i.e.
bool trivial_
Indicates whether this point representation is trivial.
virtual void copyToFloatArray(const PointTarget &p, float *out) const
Copy point data from input point to a float array.
TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset.
void setThreshold(double threshold)
Set a threshold for which a specific transformation is considered valid.
void setSearchMethodTarget(const KdTreePtr &tree, bool force_no_recompute=false)
Provide a pointer to the search object used to find correspondences in the target cloud.
virtual bool operator()(const double &score1, const double &score2) const
Comparator function for deciding which score is better after running the validation on multiple trans...
bool force_no_recompute_
A flag which, if set, means the tree operating on the target cloud will never be recomputed.
shared_ptr< const TransformationValidation< PointSource, PointTarget, Scalar > > ConstPtr
double max_range_
The maximum allowable distance between a point and its correspondence in the target in order for a co...
double threshold_
The threshold for which a specific transformation is valid.
typename TransformationValidation< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
typename TransformationValidation< PointSource, PointTarget >::PointCloudTargetConstPtr PointCloudTargetConstPtr
double validateTransformation(const PointCloudSourceConstPtr &cloud_src, const PointCloudTargetConstPtr &cloud_tgt, const Matrix4 &transformation_matrix) const
Validate the given transformation with respect to the input cloud data, and return a score.
void setMaxRange(double max_range)
Set the maximum allowable distance between a point and its correspondence in the target in order for ...
double getMaxRange()
Get the maximum allowable distance between a point and its correspondence, as set by the user.
typename TransformationValidation< PointSource, PointTarget >::PointCloudSourceConstPtr PointCloudSourceConstPtr
double getThreshold()
Get the threshold for which a specific transformation is valid.
shared_ptr< TransformationValidation< PointSource, PointTarget, Scalar > > Ptr
virtual bool isValid(const PointCloudSourceConstPtr &cloud_src, const PointCloudTargetConstPtr &cloud_tgt, const Matrix4 &transformation_matrix) const
Check if the score is valid for a specific transformation.
typename KdTree::PointRepresentationConstPtr PointRepresentationConstPtr
TransformationValidation represents the base class for methods that validate the correctness of a tra...
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition: kdtree.h:62
shared_ptr< KdTree< PointT, Tree > > Ptr
Definition: kdtree.h:75
typename PointRepresentation< PointT >::ConstPtr PointRepresentationConstPtr
Definition: kdtree.h:80
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
Defines functions, macros and traits for allocating and using memory.
Defines all the PCL and non-PCL macros used.