Point Cloud Library (PCL) 1.15.0
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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
50namespace pcl {
51namespace 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 */
74template <typename PointSource, typename PointTarget, typename Scalar = float>
76public:
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,
93 typename TransformationValidation<PointSource,
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 {}
105
107
108 /** \brief Set the maximum allowable distance between a point and its correspondence
109 * in the target in order for a correspondence to be considered \a valid. Default:
110 * double::max. \param[in] max_range the new maximum allowable distance
111 */
112 inline void
113 setMaxRange(double max_range)
114 {
115 max_range_ = max_range;
116 }
117
118 /** \brief Get the maximum allowable distance between a point and its
119 * correspondence, as set by the user.
120 */
121 inline double
123 {
124 return (max_range_);
125 }
126
127 /** \brief Provide a pointer to the search object used to find correspondences in
128 * the target cloud.
129 * \param[in] tree a pointer to the spatial search object.
130 * \param[in] force_no_recompute If set to true, this tree will NEVER be
131 * recomputed, regardless of calls to setInputTarget. Only use if you are
132 * confident that the tree will be set correctly.
133 */
134 inline void
135 setSearchMethodTarget(const KdTreePtr& tree, bool force_no_recompute = false)
136 {
137 tree_ = tree;
138 force_no_recompute_ = force_no_recompute;
139 }
140
141 /** \brief Set a threshold for which a specific transformation is considered valid.
142 *
143 * \note Since we're using MSE (Mean Squared Error) as a metric, the threshold
144 * represents the mean Euclidean distance threshold over all nearest neighbors
145 * up to max_range.
146 *
147 * \param[in] threshold the threshold for which a transformation is vali
148 */
149 inline void
150 setThreshold(double threshold)
151 {
152 threshold_ = threshold;
153 }
154
155 /** \brief Get the threshold for which a specific transformation is valid. */
156 inline double
158 {
159 return (threshold_);
160 }
161
162 /** \brief Validate the given transformation with respect to the input cloud data, and
163 * return a score.
164 *
165 * \param[in] cloud_src the source point cloud dataset
166 * \param[in] cloud_tgt the target point cloud dataset
167 * \param[out] transformation_matrix the resultant transformation matrix
168 *
169 * \return the score or confidence measure for the given
170 * transformation_matrix with respect to the input data
171 */
172 double
174 const PointCloudTargetConstPtr& cloud_tgt,
175 const Matrix4& transformation_matrix) const;
176
177 /** \brief Comparator function for deciding which score is better after running the
178 * validation on multiple transforms.
179 *
180 * \param[in] score1 the first value
181 * \param[in] score2 the second value
182 *
183 * \return true if score1 is better than score2
184 */
185 virtual bool
186 operator()(const double& score1, const double& score2) const
187 {
188 return (score1 < score2);
189 }
190
191 /** \brief Check if the score is valid for a specific transformation.
192 *
193 * \param[in] cloud_src the source point cloud dataset
194 * \param[in] cloud_tgt the target point cloud dataset
195 * \param[out] transformation_matrix the transformation matrix
196 *
197 * \return true if the transformation is valid, false otherwise.
198 */
199 virtual bool
201 const PointCloudTargetConstPtr& cloud_tgt,
202 const Matrix4& transformation_matrix) const
203 {
204 if (std::isnan(threshold_)) {
205 PCL_ERROR("[pcl::TransformationValidationEuclidean::isValid] Threshold not set! "
206 "Please use setThreshold () before continuing.\n");
207 return (false);
208 }
209
210 return (validateTransformation(cloud_src, cloud_tgt, transformation_matrix) <
211 threshold_);
212 }
213
214protected:
215 /** \brief The maximum allowable distance between a point and its correspondence in
216 * the target in order for a correspondence to be considered \a valid. Default:
217 * double::max.
218 */
220
221 /** \brief The threshold for which a specific transformation is valid.
222 * Set to NaN by default, as we must require the user to set it.
223 */
225
226 /** \brief A pointer to the spatial search object. */
228
229 /** \brief A flag which, if set, means the tree operating on the target cloud
230 * will never be recomputed*/
232
233 /** \brief Internal point representation uses only 3D coordinates for L2 */
236 using pcl::PointRepresentation<PointTarget>::trivial_;
237
238 public:
239 using Ptr = shared_ptr<MyPointRepresentation>;
240 using ConstPtr = shared_ptr<const MyPointRepresentation>;
241
243 {
244 nr_dimensions_ = 3;
245 trivial_ = true;
246 }
247
248 /** \brief Empty destructor */
249 virtual ~MyPointRepresentation() = default;
250
251 virtual void
252 copyToFloatArray(const PointTarget& p, float* out) const
253 {
254 out[0] = p.x;
255 out[1] = p.y;
256 out[2] = p.z;
257 }
258 };
259
260public:
262};
263} // namespace registration
264} // namespace pcl
265
266#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:86
Defines functions, macros and traits for allocating and using memory.
Defines all the PCL and non-PCL macros used.