Point Cloud Library (PCL) 1.13.0
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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:
72 : new_to_old_energy_ratio_ (0.99f)
73 {}
74
75 ~TrimmedICP () override = default;
76
77 /** \brief Call this method before calling align().
78 *
79 * \param[in] target is target point cloud. The method builds a kd-tree based on 'target' for performing fast closest point search.
80 * The source point cloud will be registered to 'target' (see align() method).
81 * */
82 inline void
83 init (const PointCloudConstPtr& target)
84 {
85 target_points_ = target;
86 kdtree_.setInputCloud (target);
87 }
88
89 /** \brief The method performs trimmed ICP, i.e., it rigidly registers the source to the target (passed to the init() method).
90 *
91 * \param[in] source_points is the point cloud to be registered to the target.
92 * \param[in] num_source_points_to_use gives the number of closest source points taken into account for registration. By closest
93 * source points we mean the source points closest to the target. These points are computed anew at each iteration.
94 * \param[in,out] guess_and_result is the estimated rigid transform. IMPORTANT: this matrix is also taken as the initial guess
95 * for the alignment. If there is no guess, set the matrix to identity!
96 * */
97 inline void
98 align (const PointCloud& source_points, int num_source_points_to_use, Matrix4& guess_and_result) const
99 {
100 int num_trimmed_source_points = num_source_points_to_use, num_source_points = static_cast<int> (source_points.size ());
101
102 if ( num_trimmed_source_points >= num_source_points )
103 {
104 printf ("WARNING in 'TrimmedICP::%s()': the user-defined number of source points of interest is greater or equal to "
105 "the total number of source points. Trimmed ICP will work correctly but won't be very efficient. Either set "
106 "the number of source points to use to a lower value or use standard ICP.\n", __func__);
107 num_trimmed_source_points = num_source_points;
108 }
109
110 // These are vectors containing source to target correspondences
111 pcl::Correspondences full_src_to_tgt (num_source_points), trimmed_src_to_tgt (num_trimmed_source_points);
112
113 // Some variables for the closest point search
114 pcl::PointXYZ transformed_source_point;
115 pcl::Indices target_index (1);
116 std::vector<float> sqr_dist_to_target (1);
117 float old_energy, energy = std::numeric_limits<float>::max ();
118
119// printf ("\nalign\n");
120
121 do
122 {
123 // Update the correspondences
124 for ( int i = 0 ; i < num_source_points ; ++i )
125 {
126 // Transform the i-th source point based on the current transform matrix
127 aux::transform (guess_and_result, source_points[i], transformed_source_point);
128
129 // Perform the closest point search
130 kdtree_.nearestKSearch (transformed_source_point, 1, target_index, sqr_dist_to_target);
131
132 // Update the i-th correspondence
133 full_src_to_tgt[i].index_query = i;
134 full_src_to_tgt[i].index_match = target_index[0];
135 full_src_to_tgt[i].distance = sqr_dist_to_target[0];
136 }
137
138 // Sort in ascending order according to the squared distance
139 std::sort (full_src_to_tgt.begin (), full_src_to_tgt.end (), TrimmedICP::compareCorrespondences);
140
141 old_energy = energy;
142 energy = 0.0f;
143
144 // Now, setup the trimmed correspondences used for the transform estimation
145 for ( int i = 0 ; i < num_trimmed_source_points ; ++i )
146 {
147 trimmed_src_to_tgt[i].index_query = full_src_to_tgt[i].index_query;
148 trimmed_src_to_tgt[i].index_match = full_src_to_tgt[i].index_match;
149 energy += full_src_to_tgt[i].distance;
150 }
151
152 this->estimateRigidTransformation (source_points, *target_points_, trimmed_src_to_tgt, guess_and_result);
153
154// printf ("energy = %f, energy diff. = %f, ratio = %f\n", energy, old_energy - energy, energy/old_energy);
155 }
156 while ( energy/old_energy < new_to_old_energy_ratio_ ); // iterate if enough progress
157
158// printf ("\n");
159 }
160
161 inline void
163 {
164 if ( ratio >= 1 )
165 new_to_old_energy_ratio_ = 0.99f;
166 else
167 new_to_old_energy_ratio_ = ratio;
168 }
169
170 protected:
171 static inline bool
173 {
174 return a.distance < b.distance;
175 }
176
177 protected:
181 };
182 } // namespace recognition
183} // 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:83
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:98
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.