Point Cloud Library (PCL) 1.15.0
Loading...
Searching...
No Matches
organized.h
1/*
2 * Software License Agreement (BSD License)
3 *
4 * Point Cloud Library (PCL) - www.pointclouds.org
5 * Copyright (c) 2010-2011, Willow Garage, Inc.
6 *
7 * All rights reserved.
8 *
9 * Redistribution and use in source and binary forms, with or without
10 * modification, are permitted provided that the following conditions
11 * are met:
12 *
13 * * Redistributions of source code must retain the above copyright
14 * notice, this list of conditions and the following disclaimer.
15 * * Redistributions in binary form must reproduce the above
16 * copyright notice, this list of conditions and the following
17 * disclaimer in the documentation and/or other materials provided
18 * with the distribution.
19 * * Neither the name of the copyright holder(s) nor the names of its
20 * contributors may be used to endorse or promote products derived
21 * from this software without specific prior written permission.
22 *
23 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
24 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
25 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
26 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
27 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
28 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
29 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
30 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
31 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
32 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
33 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
34 * POSSIBILITY OF SUCH DAMAGE.
35 *
36 * $Id$
37 *
38 */
39
40#pragma once
41
42#include <pcl/memory.h>
43#include <pcl/pcl_macros.h>
44#include <pcl/point_cloud.h>
45#include <pcl/common/point_tests.h> // for pcl::isFinite
46#include <pcl/search/search.h>
47#include <pcl/common/eigen.h>
48
49#include <algorithm>
50#include <vector>
51
52namespace pcl
53{
54 namespace search
55 {
56 /** \brief OrganizedNeighbor is a class for optimized nearest neighbor search in
57 * organized projectable point clouds, for instance from Time-Of-Flight cameras or
58 * stereo cameras. Note that rotating LIDARs may output organized clouds, but are
59 * not projectable via a pinhole camera model into two dimensions and thus will
60 * generally not work with this class.
61 * \author Radu B. Rusu, Julius Kammerl, Suat Gedikli, Koen Buys
62 * \ingroup search
63 */
64 template<typename PointT>
66 {
67
68 public:
69 // public typedefs
72
74
75 using Ptr = shared_ptr<pcl::search::OrganizedNeighbor<PointT> >;
76 using ConstPtr = shared_ptr<const pcl::search::OrganizedNeighbor<PointT> >;
77
81
82 /** \brief Constructor
83 * \param[in] sorted_results whether the results should be return sorted in ascending order on the distances or not.
84 * This applies only for radius search, since knn always returns sorted results
85 * \param[in] eps the threshold for the mean-squared-error of the estimation of the projection matrix.
86 * if the MSE is above this value, the point cloud is considered as not from a projective device,
87 * thus organized neighbor search can not be applied on that cloud.
88 * \param[in] pyramid_level the level of the down sampled point cloud to be used for projection matrix estimation
89 */
90 OrganizedNeighbor (bool sorted_results = false, float eps = 1e-4f, unsigned pyramid_level = 5)
91 : Search<PointT> ("OrganizedNeighbor", sorted_results)
92 , projection_matrix_ (Eigen::Matrix<float, 3, 4, Eigen::RowMajor>::Zero ())
93 , KR_ (Eigen::Matrix<float, 3, 3, Eigen::RowMajor>::Zero ())
94 , KR_KRT_ (Eigen::Matrix<float, 3, 3, Eigen::RowMajor>::Zero ())
95 , eps_ (eps)
96 , pyramid_level_ (pyramid_level)
97 {
98 }
99
100 /** \brief Empty deconstructor. */
101 ~OrganizedNeighbor () override = default;
102
103 /** \brief Test whether this search-object is valid (input is organized AND from projective device)
104 * User should use this method after setting the input cloud, since setInput just prints an error
105 * if input is not organized or a projection matrix could not be determined.
106 * \return true if the input data is organized and from a projective device, false otherwise
107 */
108 bool
109 isValid () const
110 {
111 // determinant (KR) = determinant (K) * determinant (R) = determinant (K) = f_x * f_y.
112 // If we expect at max an opening angle of 170degree in x-direction -> f_x = 2.0 * width / tan (85 degree);
113 // 2 * tan (85 degree) ~ 22.86
114 float min_f = 0.043744332f * static_cast<float>(input_->width);
115 //std::cout << "isValid: " << determinant3x3Matrix<Eigen::Matrix3f> (KR_ / sqrt (KR_KRT_.coeff (8))) << " >= " << (min_f * min_f) << std::endl;
116 return (determinant3x3Matrix<Eigen::Matrix3f> (KR_ / std::sqrt (KR_KRT_.coeff (8))) >= (min_f * min_f));
117 }
118
119 /** \brief Compute the camera matrix
120 * \param[out] camera_matrix the resultant computed camera matrix
121 */
122 void
123 computeCameraMatrix (Eigen::Matrix3f& camera_matrix) const;
124
125 /** \brief Provide a pointer to the input data set, if user has focal length he must set it before calling this
126 * \param[in] cloud the const boost shared pointer to a PointCloud message
127 * \param[in] indices the const boost shared pointer to PointIndices
128 */
129 bool
130 setInputCloud (const PointCloudConstPtr& cloud, const IndicesConstPtr &indices = IndicesConstPtr ()) override
131 {
132 input_ = cloud;
133
134 mask_.resize (input_->size ());
135 input_ = cloud;
136 indices_ = indices;
137
138 if (indices_ && !indices_->empty())
139 {
140 mask_.assign (input_->size (), 0);
141 for (const auto& idx : *indices_)
142 if (pcl::isFinite((*input_)[idx]))
143 mask_[idx] = 1;
144 }
145 else
146 {
147 mask_.assign (input_->size (), 0);
148 for (std::size_t idx=0; idx<input_->size(); ++idx)
149 if (pcl::isFinite((*input_)[idx]))
150 mask_[idx] = 1;
151 }
152
153 return estimateProjectionMatrix () && isValid ();
154 }
155
156 /** \brief Search for all neighbors of query point that are within a given radius.
157 * \param[in] p_q the given query point
158 * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
159 * \param[out] k_indices the resultant indices of the neighboring points
160 * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
161 * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
162 * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
163 * returned.
164 * \return number of neighbors found in radius
165 */
166 int
167 radiusSearch (const PointT &p_q,
168 double radius,
169 Indices &k_indices,
170 std::vector<float> &k_sqr_distances,
171 unsigned int max_nn = 0) const override;
172
173 /** \brief estimated the projection matrix from the input cloud. */
174 bool
176
177 /** \brief Search for the k-nearest neighbors for a given query point.
178 * \note limiting the maximum search radius (with setMaxDistance) can lead to a significant improvement in search speed
179 * \param[in] p_q the given query point (\ref setInputCloud must be given a-priori!)
180 * \param[in] k the number of neighbors to search for (used only if horizontal and vertical window not given already!)
181 * \param[out] k_indices the resultant point indices (must be resized to \a k beforehand!)
182 * \param[out] k_sqr_distances \note this function does not return distances
183 * \return number of neighbors found
184 * @todo still need to implements this functionality
185 */
186 int
187 nearestKSearch (const PointT &p_q,
188 int k,
189 Indices &k_indices,
190 std::vector<float> &k_sqr_distances) const override;
191
192 /** \brief projects a point into the image
193 * \param[in] p point in 3D World Coordinate Frame to be projected onto the image plane
194 * \param[out] q the 2D projected point in pixel coordinates (u,v)
195 * @return true if projection is valid, false otherwise
196 */
197 bool projectPoint (const PointT& p, pcl::PointXY& q) const;
198
199 protected:
200
201 struct Entry
202 {
203 Entry (index_t idx, float dist) : index (idx), distance (dist) {}
204 Entry () : index (0), distance (0) {}
206 float distance;
207
208 inline bool
209 operator < (const Entry& other) const
210 {
211 return (distance < other.distance);
212 }
213 };
214
215 /** \brief test if point given by index is among the k NN in results to the query point.
216 * \param[in] query query point
217 * \param[in] k number of maximum nn interested in
218 * \param[in,out] queue priority queue with k NN
219 * \param[in] index index on point to be tested
220 * \return whether the top element changed or not.
221 */
222 inline bool
223 testPoint (const PointT& query, unsigned k, std::vector<Entry>& queue, index_t index) const
224 {
225 const PointT& point = input_->points [index];
226 if (mask_ [index])
227 {
228 //float squared_distance = (point.getVector3fMap () - query.getVector3fMap ()).squaredNorm ();
229 float dist_x = point.x - query.x;
230 float dist_y = point.y - query.y;
231 float dist_z = point.z - query.z;
232 float squared_distance = dist_x * dist_x + dist_y * dist_y + dist_z * dist_z;
233 const auto queue_size = queue.size ();
234 const auto insert_into_queue = [&]{ queue.emplace (
235 std::upper_bound (queue.begin(), queue.end(), squared_distance,
236 [](float dist, const Entry& ent){ return dist<ent.distance; }),
237 index, squared_distance); };
238 if (queue_size < k)
239 {
240 insert_into_queue ();
241 return (queue_size + 1) == k;
242 }
243 if (queue.back ().distance > squared_distance)
244 {
245 queue.pop_back ();
246 insert_into_queue ();
247 return true; // top element has changed!
248 }
249 }
250 return false;
251 }
252
253 inline void
254 clipRange (int& begin, int &end, int min, int max) const
255 {
256 begin = std::max (std::min (begin, max), min);
257 end = std::min (std::max (end, min), max);
258 }
259
260 /** \brief Obtain a search box in 2D from a sphere with a radius in 3D
261 * \param[in] point the query point (sphere center)
262 * \param[in] squared_radius the squared sphere radius
263 * \param[out] minX the min X box coordinate
264 * \param[out] minY the min Y box coordinate
265 * \param[out] maxX the max X box coordinate
266 * \param[out] maxY the max Y box coordinate
267 */
268 void
269 getProjectedRadiusSearchBox (const PointT& point, float squared_radius, unsigned& minX, unsigned& minY,
270 unsigned& maxX, unsigned& maxY) const;
271
272
273 /** \brief the projection matrix. Either set by user or calculated by the first / each input cloud */
274 Eigen::Matrix<float, 3, 4, Eigen::RowMajor> projection_matrix_;
275
276 /** \brief inveser of the left 3x3 projection matrix which is K * R (with K being the camera matrix and R the rotation matrix)*/
277 Eigen::Matrix<float, 3, 3, Eigen::RowMajor> KR_;
278
279 /** \brief inveser of the left 3x3 projection matrix which is K * R (with K being the camera matrix and R the rotation matrix)*/
280 Eigen::Matrix<float, 3, 3, Eigen::RowMajor> KR_KRT_;
281
282 /** \brief epsilon value for the MSE of the projection matrix estimation*/
283 const float eps_;
284
285 /** \brief using only a subsample of points to calculate the projection matrix. pyramid_level_ = use down sampled cloud given by pyramid_level_*/
286 const unsigned pyramid_level_;
287
288 /** \brief mask, indicating whether the point was in the indices list or not, and whether it is finite.*/
289 std::vector<unsigned char> mask_;
290 public:
292 };
293 }
294}
295
296#ifdef PCL_NO_PRECOMPILE
297#include <pcl/search/impl/organized.hpp>
298#endif
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< PointCloud< PointT > > Ptr
shared_ptr< const PointCloud< PointT > > ConstPtr
OrganizedNeighbor is a class for optimized nearest neighbor search in organized projectable point clo...
Definition organized.h:66
bool estimateProjectionMatrix()
estimated the projection matrix from the input cloud.
typename PointCloud::ConstPtr PointCloudConstPtr
Definition organized.h:73
int radiusSearch(const PointT &p_q, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const override
Search for all neighbors of query point that are within a given radius.
Definition organized.hpp:49
int nearestKSearch(const PointT &p_q, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
Search for the k-nearest neighbors for a given query point.
bool isValid() const
Test whether this search-object is valid (input is organized AND from projective device) User should ...
Definition organized.h:109
Eigen::Matrix< float, 3, 3, Eigen::RowMajor > KR_
inveser of the left 3x3 projection matrix which is K * R (with K being the camera matrix and R the ro...
Definition organized.h:277
shared_ptr< const pcl::search::OrganizedNeighbor< PointT > > ConstPtr
Definition organized.h:76
void computeCameraMatrix(Eigen::Matrix3f &camera_matrix) const
Compute the camera matrix.
bool testPoint(const PointT &query, unsigned k, std::vector< Entry > &queue, index_t index) const
test if point given by index is among the k NN in results to the query point.
Definition organized.h:223
std::vector< unsigned char > mask_
mask, indicating whether the point was in the indices list or not, and whether it is finite.
Definition organized.h:289
void clipRange(int &begin, int &end, int min, int max) const
Definition organized.h:254
Eigen::Matrix< float, 3, 4, Eigen::RowMajor > projection_matrix_
the projection matrix.
Definition organized.h:274
typename PointCloud::Ptr PointCloudPtr
Definition organized.h:71
const unsigned pyramid_level_
using only a subsample of points to calculate the projection matrix.
Definition organized.h:286
Eigen::Matrix< float, 3, 3, Eigen::RowMajor > KR_KRT_
inveser of the left 3x3 projection matrix which is K * R (with K being the camera matrix and R the ro...
Definition organized.h:280
shared_ptr< pcl::search::OrganizedNeighbor< PointT > > Ptr
Definition organized.h:75
OrganizedNeighbor(bool sorted_results=false, float eps=1e-4f, unsigned pyramid_level=5)
Constructor.
Definition organized.h:90
bool setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
Provide a pointer to the input data set, if user has focal length he must set it before calling this.
Definition organized.h:130
bool projectPoint(const PointT &p, pcl::PointXY &q) const
projects a point into the image
void getProjectedRadiusSearchBox(const PointT &point, float squared_radius, unsigned &minX, unsigned &minY, unsigned &maxX, unsigned &maxY) const
Obtain a search box in 2D from a sphere with a radius in 3D.
~OrganizedNeighbor() override=default
Empty deconstructor.
const float eps_
epsilon value for the MSE of the projection matrix estimation
Definition organized.h:283
Generic search class.
Definition search.h:75
PointCloudConstPtr input_
Definition search.h:402
IndicesConstPtr indices_
Definition search.h:403
pcl::IndicesConstPtr IndicesConstPtr
Definition search.h:85
#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.
Definition bfgs.h:10
bool isFinite(const PointT &pt)
Tests if the 3D components of a point are all finite param[in] pt point to be tested return true if f...
Definition point_tests.h:55
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition types.h:112
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
Defines all the PCL and non-PCL macros used.
A 2D point structure representing Euclidean xy coordinates.
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition organized.h:202
bool operator<(const Entry &other) const
Definition organized.h:209
float distance
Definition organized.h:206
Entry()
Definition organized.h:204
index_t index
Definition organized.h:205
Entry(index_t idx, float dist)
Definition organized.h:203