Point Cloud Library (PCL) 1.13.1
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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 , 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
215protected:
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 */
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 */
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 */
237 using pcl::PointRepresentation<PointTarget>::trivial_;
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
261public:
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.