Point Cloud Library (PCL) 1.15.0
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correspondence_rejection_sample_consensus.h
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40
41#pragma once
42
43#include <pcl/registration/correspondence_rejection.h>
44#include <pcl/memory.h>
45
46namespace pcl {
47namespace registration {
48/** \brief CorrespondenceRejectorSampleConsensus implements a correspondence rejection
49 * using Random Sample Consensus to identify inliers (and reject outliers)
50 * \author Dirk Holz
51 * \ingroup registration
52 */
53template <typename PointT>
55 using PointCloud = pcl::PointCloud<PointT>;
56 using PointCloudPtr = typename PointCloud::Ptr;
57 using PointCloudConstPtr = typename PointCloud::ConstPtr;
58
59public:
63
64 using Ptr = shared_ptr<CorrespondenceRejectorSampleConsensus<PointT>>;
65 using ConstPtr = shared_ptr<const CorrespondenceRejectorSampleConsensus<PointT>>;
66
67 /** \brief Empty constructor. Sets the inlier threshold to 5cm (0.05m),
68 * and the maximum number of iterations to 1000.
69 */
71 {
72 rejection_name_ = "CorrespondenceRejectorSampleConsensus";
73 }
74
75 /** \brief Empty destructor. */
77
78 /** \brief Get a list of valid correspondences after rejection from the original set
79 * of correspondences. \param[in] original_correspondences the set of initial
80 * correspondences given \param[out] remaining_correspondences the resultant filtered
81 * set of remaining correspondences
82 */
83 inline void
84 getRemainingCorrespondences(const pcl::Correspondences& original_correspondences,
85 pcl::Correspondences& remaining_correspondences) override;
86
87 /** \brief Provide a source point cloud dataset (must contain XYZ data!)
88 * \param[in] cloud a cloud containing XYZ data
89 */
90 virtual inline void
91 setInputSource(const PointCloudConstPtr& cloud)
92 {
93 input_ = cloud;
94 }
95
96 /** \brief Get a pointer to the input point cloud dataset target. */
97 inline PointCloudConstPtr const
99 {
100 return (input_);
101 }
102
103 /** \brief Provide a target point cloud dataset (must contain XYZ data!)
104 * \param[in] cloud a cloud containing XYZ data
105 */
106 virtual inline void
107 setInputTarget(const PointCloudConstPtr& cloud)
108 {
109 target_ = cloud;
110 }
111
112 /** \brief Get a pointer to the input point cloud dataset target. */
113 inline PointCloudConstPtr const
115 {
116 return (target_);
117 }
118
119 /** \brief See if this rejector requires source points */
120 bool
121 requiresSourcePoints() const override
122 {
123 return (true);
124 }
125
126 /** \brief Blob method for setting the source cloud */
127 void
129 {
130 PointCloudPtr cloud(new PointCloud);
131 fromPCLPointCloud2(*cloud2, *cloud);
132 setInputSource(cloud);
133 }
134
135 /** \brief See if this rejector requires a target cloud */
136 bool
137 requiresTargetPoints() const override
138 {
139 return (true);
140 }
141
142 /** \brief Method for setting the target cloud */
143 void
145 {
146 PointCloudPtr cloud(new PointCloud);
147 fromPCLPointCloud2(*cloud2, *cloud);
148 setInputTarget(cloud);
149 }
150
151 /** \brief Set the maximum distance between corresponding points.
152 * Correspondences with distances below the threshold are considered as inliers.
153 * \param[in] threshold Distance threshold in the same dimension as source and target
154 * data sets.
155 */
156 inline void
157 setInlierThreshold(double threshold)
158 {
159 inlier_threshold_ = threshold;
160 };
161
162 /** \brief Get the maximum distance between corresponding points.
163 * \return Distance threshold in the same dimension as source and target data sets.
164 */
165 inline double
167 {
168 return inlier_threshold_;
169 };
170
171 /** \brief Set the maximum number of iterations.
172 * \param[in] max_iterations Maximum number if iterations to run
173 */
174 inline void
175 setMaximumIterations(int max_iterations)
176 {
177 max_iterations_ = std::max(max_iterations, 0);
178 }
179
180 /** \brief Get the maximum number of iterations.
181 * \return max_iterations Maximum number if iterations to run
182 */
183 inline int
185 {
186 return (max_iterations_);
187 }
188
189 /** \brief Get the best transformation after RANSAC rejection.
190 * \return The homogeneous 4x4 transformation yielding the largest number of inliers.
191 */
192 inline Eigen::Matrix4f
194 {
196 };
197
198 /** \brief Specify whether the model should be refined internally using the variance
199 * of the inliers \param[in] refine true if the model should be refined, false
200 * otherwise
201 */
202 inline void
203 setRefineModel(const bool refine)
204 {
205 refine_ = refine;
206 }
207
208 /** \brief Get the internal refine parameter value as set by the user using
209 * setRefineModel */
210 inline bool
212 {
213 return (refine_);
214 }
215
216 /** \brief Get the inlier indices found by the correspondence rejector. This
217 * information is only saved if setSaveInliers(true) was called in advance.
218 * \param[out] inlier_indices Indices for the inliers
219 */
220 inline void
222 {
223 inlier_indices = inlier_indices_;
224 }
225
226 /** \brief Set whether to save inliers or not
227 * \param[in] s True to save inliers / False otherwise
228 */
229 inline void
231 {
232 save_inliers_ = s;
233 }
234
235 /** \brief Get whether the rejector is configured to save inliers */
236 inline bool
238 {
239 return save_inliers_;
240 }
241
242protected:
243 /** \brief Apply the rejection algorithm.
244 * \param[out] correspondences the set of resultant correspondences.
245 */
246 inline void
247 applyRejection(pcl::Correspondences& correspondences) override
248 {
250 }
251
252 double inlier_threshold_{0.05};
253
255
256 PointCloudConstPtr input_;
257 PointCloudPtr input_transformed_;
258 PointCloudConstPtr target_;
259
260 Eigen::Matrix4f best_transformation_;
261
262 bool refine_{false};
264 bool save_inliers_{false};
265
266public:
268};
269} // namespace registration
270} // namespace pcl
271
272#include <pcl/registration/impl/correspondence_rejection_sample_consensus.hpp>
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< PointCloud< PointT > > Ptr
shared_ptr< const PointCloud< PointT > > ConstPtr
CorrespondenceRejector represents the base class for correspondence rejection methods
CorrespondencesConstPtr input_correspondences_
The input correspondences.
std::string rejection_name_
The name of the rejection method.
const std::string & getClassName() const
Get a string representation of the name of this class.
CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Conse...
bool requiresSourcePoints() const override
See if this rejector requires source points.
double getInlierThreshold()
Get the maximum distance between corresponding points.
virtual void setInputSource(const PointCloudConstPtr &cloud)
Provide a source point cloud dataset (must contain XYZ data!)
shared_ptr< CorrespondenceRejectorSampleConsensus< PointT > > Ptr
Eigen::Matrix4f getBestTransformation()
Get the best transformation after RANSAC rejection.
bool getSaveInliers()
Get whether the rejector is configured to save inliers.
virtual void setInputTarget(const PointCloudConstPtr &cloud)
Provide a target point cloud dataset (must contain XYZ data!)
void setInlierThreshold(double threshold)
Set the maximum distance between corresponding points.
void setMaximumIterations(int max_iterations)
Set the maximum number of iterations.
void getRemainingCorrespondences(const pcl::Correspondences &original_correspondences, pcl::Correspondences &remaining_correspondences) override
Get a list of valid correspondences after rejection from the original set of correspondences.
void setTargetPoints(pcl::PCLPointCloud2::ConstPtr cloud2) override
Method for setting the target cloud.
CorrespondencesConstPtr input_correspondences_
The input correspondences.
shared_ptr< const CorrespondenceRejectorSampleConsensus< PointT > > ConstPtr
bool requiresTargetPoints() const override
See if this rejector requires a target cloud.
bool getRefineModel() const
Get the internal refine parameter value as set by the user using setRefineModel.
~CorrespondenceRejectorSampleConsensus() override=default
Empty destructor.
PointCloudConstPtr const getInputSource()
Get a pointer to the input point cloud dataset target.
std::string rejection_name_
The name of the rejection method.
PointCloudConstPtr const getInputTarget()
Get a pointer to the input point cloud dataset target.
void applyRejection(pcl::Correspondences &correspondences) override
Apply the rejection algorithm.
void getInliersIndices(pcl::Indices &inlier_indices)
Get the inlier indices found by the correspondence rejector.
void setSourcePoints(pcl::PCLPointCloud2::ConstPtr cloud2) override
Blob method for setting the source cloud.
void setRefineModel(const bool refine)
Specify whether the model should be refined internally using the variance of the inliers.
#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.
void fromPCLPointCloud2(const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map, const std::uint8_t *msg_data)
Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
shared_ptr< const ::pcl::PCLPointCloud2 > ConstPtr