Point Cloud Library (PCL) 1.14.0
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trimmed_icp.h
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39
40/*
41 * trimmed_icp.h
42 *
43 * Created on: Mar 10, 2013
44 * Author: papazov
45 */
46
47#pragma once
48
49#include <pcl/registration/transformation_estimation_svd.h>
50#include <pcl/kdtree/kdtree_flann.h>
51#include <pcl/correspondence.h>
52#include <pcl/point_cloud.h>
53#include <pcl/pcl_exports.h>
54#include <limits>
55#include <pcl/recognition/ransac_based/auxiliary.h>
56
57namespace pcl
58{
59 namespace recognition
60 {
61 template<typename PointT, typename Scalar>
62 class PCL_EXPORTS TrimmedICP: public pcl::registration::TransformationEstimationSVD<PointT, PointT, Scalar>
63 {
64 public:
67
68 using Matrix4 = typename Eigen::Matrix<Scalar, 4, 4>;
69
70 public:
71 TrimmedICP () = default;
72
73 ~TrimmedICP () override = default;
74
75 /** \brief Call this method before calling align().
76 *
77 * \param[in] target is target point cloud. The method builds a kd-tree based on 'target' for performing fast closest point search.
78 * The source point cloud will be registered to 'target' (see align() method).
79 * */
80 inline void
81 init (const PointCloudConstPtr& target)
82 {
83 target_points_ = target;
84 kdtree_.setInputCloud (target);
85 }
86
87 /** \brief The method performs trimmed ICP, i.e., it rigidly registers the source to the target (passed to the init() method).
88 *
89 * \param[in] source_points is the point cloud to be registered to the target.
90 * \param[in] num_source_points_to_use gives the number of closest source points taken into account for registration. By closest
91 * source points we mean the source points closest to the target. These points are computed anew at each iteration.
92 * \param[in,out] guess_and_result is the estimated rigid transform. IMPORTANT: this matrix is also taken as the initial guess
93 * for the alignment. If there is no guess, set the matrix to identity!
94 * */
95 inline void
96 align (const PointCloud& source_points, int num_source_points_to_use, Matrix4& guess_and_result) const
97 {
98 int num_trimmed_source_points = num_source_points_to_use, num_source_points = static_cast<int> (source_points.size ());
99
100 if ( num_trimmed_source_points >= num_source_points )
101 {
102 printf ("WARNING in 'TrimmedICP::%s()': the user-defined number of source points of interest is greater or equal to "
103 "the total number of source points. Trimmed ICP will work correctly but won't be very efficient. Either set "
104 "the number of source points to use to a lower value or use standard ICP.\n", __func__);
105 num_trimmed_source_points = num_source_points;
106 }
107
108 // These are vectors containing source to target correspondences
109 pcl::Correspondences full_src_to_tgt (num_source_points), trimmed_src_to_tgt (num_trimmed_source_points);
110
111 // Some variables for the closest point search
112 pcl::PointXYZ transformed_source_point;
113 pcl::Indices target_index (1);
114 std::vector<float> sqr_dist_to_target (1);
115 float old_energy, energy = std::numeric_limits<float>::max ();
116
117// printf ("\nalign\n");
118
119 do
120 {
121 // Update the correspondences
122 for ( int i = 0 ; i < num_source_points ; ++i )
123 {
124 // Transform the i-th source point based on the current transform matrix
125 aux::transform (guess_and_result, source_points[i], transformed_source_point);
126
127 // Perform the closest point search
128 kdtree_.nearestKSearch (transformed_source_point, 1, target_index, sqr_dist_to_target);
129
130 // Update the i-th correspondence
131 full_src_to_tgt[i].index_query = i;
132 full_src_to_tgt[i].index_match = target_index[0];
133 full_src_to_tgt[i].distance = sqr_dist_to_target[0];
134 }
135
136 // Sort in ascending order according to the squared distance
137 std::sort (full_src_to_tgt.begin (), full_src_to_tgt.end (), TrimmedICP::compareCorrespondences);
138
139 old_energy = energy;
140 energy = 0.0f;
141
142 // Now, setup the trimmed correspondences used for the transform estimation
143 for ( int i = 0 ; i < num_trimmed_source_points ; ++i )
144 {
145 trimmed_src_to_tgt[i].index_query = full_src_to_tgt[i].index_query;
146 trimmed_src_to_tgt[i].index_match = full_src_to_tgt[i].index_match;
147 energy += full_src_to_tgt[i].distance;
148 }
149
150 this->estimateRigidTransformation (source_points, *target_points_, trimmed_src_to_tgt, guess_and_result);
151
152// printf ("energy = %f, energy diff. = %f, ratio = %f\n", energy, old_energy - energy, energy/old_energy);
153 }
154 while ( energy/old_energy < new_to_old_energy_ratio_ ); // iterate if enough progress
155
156// printf ("\n");
157 }
158
159 inline void
161 {
162 if ( ratio >= 1 )
163 new_to_old_energy_ratio_ = 0.99f;
164 else
165 new_to_old_energy_ratio_ = ratio;
166 }
167
168 protected:
169 static inline bool
171 {
172 return a.distance < b.distance;
173 }
174
175 protected:
178 float new_to_old_energy_ratio_{0.99f};
179 };
180 } // namespace recognition
181} // namespace pcl
KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures.
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< const PointCloud< PointT > > ConstPtr
~TrimmedICP() override=default
pcl::KdTreeFLANN< PointT > kdtree_
pcl::PointCloud< PointT > PointCloud
Definition trimmed_icp.h:65
PointCloudConstPtr target_points_
typename PointCloud::ConstPtr PointCloudConstPtr
Definition trimmed_icp.h:66
void init(const PointCloudConstPtr &target)
Call this method before calling align().
Definition trimmed_icp.h:81
static bool compareCorrespondences(const pcl::Correspondence &a, const pcl::Correspondence &b)
typename Eigen::Matrix< Scalar, 4, 4 > Matrix4
Definition trimmed_icp.h:68
void setNewToOldEnergyRatio(float ratio)
void align(const PointCloud &source_points, int num_source_points_to_use, Matrix4 &guess_and_result) const
The method performs trimmed ICP, i.e., it rigidly registers the source to the target (passed to the i...
Definition trimmed_icp.h:96
TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given ...
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
Correspondence represents a match between two entities (e.g., points, descriptors,...
A point structure representing Euclidean xyz coordinates.