util3d_features.cpp
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1 /*
2 Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
3 All rights reserved.
4 
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14  derived from this software without specific prior written permission.
15 
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24 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
25 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
26 */
27 
28 #include "rtabmap/core/util3d_features.h"
29 
30 #include "rtabmap/core/util2d.h"
31 #include "rtabmap/core/util3d.h"
32 #include "rtabmap/core/util3d_transforms.h"
33 #include "rtabmap/core/util3d_correspondences.h"
34 #include "rtabmap/core/util3d_motion_estimation.h"
35 
36 #include "rtabmap/core/EpipolarGeometry.h"
37 #include "opencv/five-point.h"
38 
39 #include <rtabmap/utilite/ULogger.h>
40 #include <rtabmap/utilite/UConversion.h>
41 #include <rtabmap/utilite/UMath.h>
42 #include <rtabmap/utilite/UStl.h>
43 
44 #include <pcl/common/point_tests.h>
45 
46 #include <opencv2/video/tracking.hpp>
47 
48 namespace rtabmap
49 {
50 
51 namespace util3d
52 {
53 
54 std::vector<cv::Point3f> generateKeypoints3DDepth(
55  const std::vector<cv::KeyPoint> & keypoints,
56  const cv::Mat & depth,
57  const CameraModel & cameraModel,
58  float minDepth,
59  float maxDepth)
60 {
61  UASSERT(cameraModel.isValidForProjection());
62  std::vector<CameraModel> models;
63  models.push_back(cameraModel);
64  return generateKeypoints3DDepth(keypoints, depth, models, minDepth, maxDepth);
65 }
66 
67 std::vector<cv::Point3f> generateKeypoints3DDepth(
68  const std::vector<cv::KeyPoint> & keypoints,
69  const cv::Mat & depth,
70  const std::vector<CameraModel> & cameraModels,
71  float minDepth,
72  float maxDepth)
73 {
74  UASSERT(!depth.empty() && (depth.type() == CV_32FC1 || depth.type() == CV_16UC1));
75  UASSERT(cameraModels.size());
76  std::vector<cv::Point3f> keypoints3d;
77  if(!depth.empty())
78  {
79  UASSERT(int((depth.cols/cameraModels.size())*cameraModels.size()) == depth.cols);
80  float subImageWidth = depth.cols/cameraModels.size();
81  keypoints3d.resize(keypoints.size());
82  float rgbToDepthFactorX = 1.0f/(cameraModels[0].imageWidth()>0?float(cameraModels[0].imageWidth())/subImageWidth:1.0f);
83  float rgbToDepthFactorY = 1.0f/(cameraModels[0].imageHeight()>0?float(cameraModels[0].imageHeight())/float(depth.rows):1.0f);
84  float bad_point = std::numeric_limits<float>::quiet_NaN ();
85  for(unsigned int i=0; i<keypoints.size(); ++i)
86  {
87  float x = keypoints[i].pt.x*rgbToDepthFactorX;
88  float y = keypoints[i].pt.y*rgbToDepthFactorY;
89  int cameraIndex = int(x / subImageWidth);
90  UASSERT_MSG(cameraIndex >= 0 && cameraIndex < (int)cameraModels.size(),
91  uFormat("cameraIndex=%d, models=%d, kpt.x=%f, subImageWidth=%f (Camera model image width=%d)",
92  cameraIndex, (int)cameraModels.size(), keypoints[i].pt.x, subImageWidth, cameraModels[0].imageWidth()).c_str());
93 
94  pcl::PointXYZ ptXYZ = util3d::projectDepthTo3D(
95  cameraModels.size()==1?depth:cv::Mat(depth, cv::Range::all(), cv::Range(subImageWidth*cameraIndex,subImageWidth*(cameraIndex+1))),
96  x-subImageWidth*cameraIndex,
97  y,
98  cameraModels.at(cameraIndex).cx()*rgbToDepthFactorX,
99  cameraModels.at(cameraIndex).cy()*rgbToDepthFactorY,
100  cameraModels.at(cameraIndex).fx()*rgbToDepthFactorX,
101  cameraModels.at(cameraIndex).fy()*rgbToDepthFactorY,
102  true);
103 
104  cv::Point3f pt(bad_point, bad_point, bad_point);
105  if(pcl::isFinite(ptXYZ) &&
106  (minDepth < 0.0f || ptXYZ.z > minDepth) &&
107  (maxDepth <= 0.0f || ptXYZ.z <= maxDepth))
108  {
109  pt = cv::Point3f(ptXYZ.x, ptXYZ.y, ptXYZ.z);
110  if(!cameraModels.at(cameraIndex).localTransform().isNull() &&
111  !cameraModels.at(cameraIndex).localTransform().isIdentity())
112  {
113  pt = util3d::transformPoint(pt, cameraModels.at(cameraIndex).localTransform());
114  }
115  }
116  keypoints3d.at(i) = pt;
117  }
118  }
119  return keypoints3d;
120 }
121 
122 std::vector<cv::Point3f> generateKeypoints3DDisparity(
123  const std::vector<cv::KeyPoint> & keypoints,
124  const cv::Mat & disparity,
125  const StereoCameraModel & stereoCameraModel,
126  float minDepth,
127  float maxDepth)
128 {
129  UASSERT(!disparity.empty() && (disparity.type() == CV_16SC1 || disparity.type() == CV_32F));
130  UASSERT(stereoCameraModel.isValidForProjection());
131  std::vector<cv::Point3f> keypoints3d;
132  keypoints3d.resize(keypoints.size());
133  float bad_point = std::numeric_limits<float>::quiet_NaN ();
134  for(unsigned int i=0; i!=keypoints.size(); ++i)
135  {
136  cv::Point3f tmpPt = util3d::projectDisparityTo3D(
137  keypoints[i].pt,
138  disparity,
139  stereoCameraModel);
140 
141  cv::Point3f pt(bad_point, bad_point, bad_point);
142  if(util3d::isFinite(tmpPt) &&
143  (minDepth < 0.0f || tmpPt.z > minDepth) &&
144  (maxDepth <= 0.0f || tmpPt.z <= maxDepth))
145  {
146  pt = tmpPt;
147  if(!stereoCameraModel.left().localTransform().isNull() &&
148  !stereoCameraModel.left().localTransform().isIdentity())
149  {
150  pt = util3d::transformPoint(pt, stereoCameraModel.left().localTransform());
151  }
152  }
153  keypoints3d.at(i) = pt;
154  }
155  return keypoints3d;
156 }
157 
158 std::vector<cv::Point3f> generateKeypoints3DStereo(
159  const std::vector<cv::Point2f> & leftCorners,
160  const std::vector<cv::Point2f> & rightCorners,
161  const StereoCameraModel & model,
162  const std::vector<unsigned char> & mask,
163  float minDepth,
164  float maxDepth)
165 {
166  UASSERT(leftCorners.size() == rightCorners.size());
167  UASSERT(mask.size() == 0 || leftCorners.size() == mask.size());
168  UASSERT(model.left().fx()> 0.0f && model.baseline() > 0.0f);
169 
170  std::vector<cv::Point3f> keypoints3d;
171  keypoints3d.resize(leftCorners.size());
172  float bad_point = std::numeric_limits<float>::quiet_NaN ();
173  for(unsigned int i=0; i<leftCorners.size(); ++i)
174  {
175  cv::Point3f pt(bad_point, bad_point, bad_point);
176  if(mask.empty() || mask[i])
177  {
178  float disparity = leftCorners[i].x - rightCorners[i].x;
179  if(disparity != 0.0f)
180  {
181  cv::Point3f tmpPt = util3d::projectDisparityTo3D(
182  leftCorners[i],
183  disparity,
184  model);
185 
186  if(util3d::isFinite(tmpPt) &&
187  (minDepth < 0.0f || tmpPt.z > minDepth) &&
188  (maxDepth <= 0.0f || tmpPt.z <= maxDepth))
189  {
190  pt = tmpPt;
191  if(!model.localTransform().isNull() &&
192  !model.localTransform().isIdentity())
193  {
194  pt = util3d::transformPoint(pt, model.localTransform());
195  }
196  }
197  }
198  }
199 
200  keypoints3d.at(i) = pt;
201  }
202  return keypoints3d;
203 }
204 
205 // cameraTransform, from ref to next
206 // return 3D points in ref referential
207 // If cameraTransform is not null, it will be used for triangulation instead of the camera transform computed by epipolar geometry
208 // when refGuess3D is passed and cameraTransform is null, scale will be estimated, returning scaled cloud and camera transform
209 std::map<int, cv::Point3f> generateWords3DMono(
210  const std::map<int, cv::KeyPoint> & refWords,
211  const std::map<int, cv::KeyPoint> & nextWords,
212  const CameraModel & cameraModel,
213  Transform & cameraTransform,
214  float ransacReprojThreshold,
215  float ransacConfidence,
216  const std::map<int, cv::Point3f> & refGuess3D,
217  double * varianceOut,
218  std::vector<int> * matchesOut)
219 {
220  UASSERT(cameraModel.isValidForProjection());
221  std::map<int, cv::Point3f> words3D;
222  std::list<std::pair<int, std::pair<cv::KeyPoint, cv::KeyPoint> > > pairs;
223  int pairsFound = EpipolarGeometry::findPairs(refWords, nextWords, pairs);
224  UDEBUG("pairsFound=%d/%d", pairsFound, int(refWords.size()>nextWords.size()?refWords.size():nextWords.size()));
225  if(pairsFound > 8)
226  {
227  std::list<std::pair<int, std::pair<cv::KeyPoint, cv::KeyPoint> > >::iterator iter=pairs.begin();
228  std::vector<cv::Point2f> refCorners(pairs.size());
229  std::vector<cv::Point2f> newCorners(pairs.size());
230  std::vector<int> indexes(pairs.size());
231  for(unsigned int i=0; i<pairs.size(); ++i)
232  {
233  if(matchesOut)
234  {
235  matchesOut->push_back(iter->first);
236  }
237 
238  refCorners[i] = iter->second.first.pt;
239  newCorners[i] = iter->second.second.pt;
240  indexes[i] = iter->first;
241  ++iter;
242  }
243 
244  std::vector<unsigned char> status;
245  cv::Mat pts4D;
246 
247  UDEBUG("Five-point algorithm");
252  cv::Mat E = cv3::findEssentialMat(refCorners, newCorners, cameraModel.K(), cv::RANSAC, ransacConfidence, ransacReprojThreshold, status);
253 
254  int essentialInliers = 0;
255  for(size_t i=0; i<status.size();++i)
256  {
257  if(status[i])
258  {
259  ++essentialInliers;
260  }
261  }
262  Transform cameraTransformGuess = cameraTransform;
263  if(!E.empty())
264  {
265  UDEBUG("essential inliers=%d/%d", essentialInliers, (int)status.size());
266  cv::Mat R,t;
267  cv3::recoverPose(E, refCorners, newCorners, cameraModel.K(), R, t, 50, status, pts4D);
268  if(!R.empty() && !t.empty())
269  {
270  cv::Mat P = cv::Mat::zeros(3, 4, CV_64FC1);
271  R.copyTo(cv::Mat(P, cv::Range(0,3), cv::Range(0,3)));
272  P.at<double>(0,3) = t.at<double>(0);
273  P.at<double>(1,3) = t.at<double>(1);
274  P.at<double>(2,3) = t.at<double>(2);
275 
276  cameraTransform = Transform(R.at<double>(0,0), R.at<double>(0,1), R.at<double>(0,2), t.at<double>(0),
277  R.at<double>(1,0), R.at<double>(1,1), R.at<double>(1,2), t.at<double>(1),
278  R.at<double>(2,0), R.at<double>(2,1), R.at<double>(2,2), t.at<double>(2));
279  UDEBUG("t (cam frame)=%s", cameraTransform.prettyPrint().c_str());
280  UDEBUG("base->cam=%s", cameraModel.localTransform().prettyPrint().c_str());
281  cameraTransform = cameraModel.localTransform() * cameraTransform.inverse() * cameraModel.localTransform().inverse();
282  UDEBUG("t (base frame)=%s", cameraTransform.prettyPrint().c_str());
283 
284  UASSERT((int)indexes.size() == pts4D.cols && pts4D.rows == 4 && status.size() == indexes.size());
285  for(unsigned int i=0; i<indexes.size(); ++i)
286  {
287  if(status[i])
288  {
289  pts4D.col(i) /= pts4D.at<double>(3,i);
290  if(pts4D.at<double>(2,i) > 0)
291  {
292  words3D.insert(std::make_pair(indexes[i], util3d::transformPoint(cv::Point3f(pts4D.at<double>(0,i), pts4D.at<double>(1,i), pts4D.at<double>(2,i)), cameraModel.localTransform())));
293  }
294  }
295  }
296  }
297  }
298  else
299  {
300  UDEBUG("Failed to find essential matrix");
301  }
302 
303  if(!cameraTransform.isNull())
304  {
305  UDEBUG("words3D=%d refGuess3D=%d cameraGuess=%s", (int)words3D.size(), (int)refGuess3D.size(), cameraTransformGuess.prettyPrint().c_str());
306 
307  // estimate the scale and variance
308  float scale = 1.0f;
309  if(!cameraTransformGuess.isNull())
310  {
311  scale = cameraTransformGuess.getNorm()/cameraTransform.getNorm();
312  }
313  float variance = 1.0f;
314 
315  std::vector<cv::Point3f> inliersRef;
316  std::vector<cv::Point3f> inliersRefGuess;
317  if(!refGuess3D.empty())
318  {
319  util3d::findCorrespondences(
320  words3D,
321  refGuess3D,
322  inliersRef,
323  inliersRefGuess,
324  0);
325  }
326 
327  if(!inliersRef.empty())
328  {
329  UDEBUG("inliersRef=%d", (int)inliersRef.size());
330  if(cameraTransformGuess.isNull())
331  {
332  std::multimap<float, float> scales; // <variance, scale>
333  for(unsigned int i=0; i<inliersRef.size(); ++i)
334  {
335  // using x as depth, assuming we are in global referential
336  float s = inliersRefGuess.at(i).x/inliersRef.at(i).x;
337  std::vector<float> errorSqrdDists(inliersRef.size());
338  for(unsigned int j=0; j<inliersRef.size(); ++j)
339  {
340  cv::Point3f refPt = inliersRef.at(j);
341  refPt.x *= s;
342  refPt.y *= s;
343  refPt.z *= s;
344  const cv::Point3f & newPt = inliersRefGuess.at(j);
345  errorSqrdDists[j] = uNormSquared(refPt.x-newPt.x, refPt.y-newPt.y, refPt.z-newPt.z);
346  }
347  std::sort(errorSqrdDists.begin(), errorSqrdDists.end());
348  double median_error_sqr = (double)errorSqrdDists[errorSqrdDists.size () >> 2];
349  float var = 2.1981 * median_error_sqr;
350  //UDEBUG("scale %d = %f variance = %f", (int)i, s, variance);
351 
352  scales.insert(std::make_pair(var, s));
353  }
354  scale = scales.begin()->second;
355  variance = scales.begin()->first;
356  }
357  else if(!cameraTransformGuess.isNull())
358  {
359  // use scale from guess
360  //compute variance
361  std::vector<float> errorSqrdDists(inliersRef.size());
362  for(unsigned int j=0; j<inliersRef.size(); ++j)
363  {
364  cv::Point3f refPt = inliersRef.at(j);
365  refPt.x *= scale;
366  refPt.y *= scale;
367  refPt.z *= scale;
368  const cv::Point3f & newPt = inliersRefGuess.at(j);
369  errorSqrdDists[j] = uNormSquared(refPt.x-newPt.x, refPt.y-newPt.y, refPt.z-newPt.z);
370  }
371  std::sort(errorSqrdDists.begin(), errorSqrdDists.end());
372  double median_error_sqr = (double)errorSqrdDists[errorSqrdDists.size () >> 2];
373  variance = 2.1981 * median_error_sqr;
374  }
375  }
376  else if(!refGuess3D.empty())
377  {
378  UWARN("Cannot compute variance, no points corresponding between "
379  "the generated ref words (%d) and words guess (%d)",
380  (int)words3D.size(), (int)refGuess3D.size());
381  }
382 
383  if(scale!=1.0f)
384  {
385  // Adjust output transform and points based on scale found
386  cameraTransform.x()*=scale;
387  cameraTransform.y()*=scale;
388  cameraTransform.z()*=scale;
389 
390  UASSERT(indexes.size() == newCorners.size());
391  for(unsigned int i=0; i<indexes.size(); ++i)
392  {
393  std::map<int, cv::Point3f>::iterator iter = words3D.find(indexes[i]);
394  if(iter!=words3D.end() && util3d::isFinite(iter->second))
395  {
396  iter->second.x *= scale;
397  iter->second.y *= scale;
398  iter->second.z *= scale;
399  }
400  }
401  }
402  UDEBUG("scale used = %f (variance=%f)", scale, variance);
403  if(varianceOut)
404  {
405  *varianceOut = variance;
406  }
407  }
408  }
409  UDEBUG("wordsSet=%d / %d", (int)words3D.size(), pairsFound);
410 
411  return words3D;
412 }
413 
414 std::multimap<int, cv::KeyPoint> aggregate(
415  const std::list<int> & wordIds,
416  const std::vector<cv::KeyPoint> & keypoints)
417 {
418  std::multimap<int, cv::KeyPoint> words;
419  std::vector<cv::KeyPoint>::const_iterator kpIter = keypoints.begin();
420  for(std::list<int>::const_iterator iter=wordIds.begin(); iter!=wordIds.end(); ++iter)
421  {
422  words.insert(std::pair<int, cv::KeyPoint >(*iter, *kpIter));
423  ++kpIter;
424  }
425  return words;
426 }
427 
428 }
429 
430 }
glm::mask
GLM_FUNC_DECL genIType mask(genIType const &count)
rtabmap::util3d::isFinite
bool RTABMAP_EXP isFinite(const cv::Point3f &pt)
Definition: util3d.cpp:3194
rtabmap::util3d::projectDisparityTo3D
cv::Point3f RTABMAP_EXP projectDisparityTo3D(const cv::Point2f &pt, float disparity, const StereoCameraModel &model)
Definition: util3d.cpp:2521
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cv::Point3f RTABMAP_EXP transformPoint(const cv::Point3f &pt, const Transform &transform)
Definition: util3d_transforms.cpp:211
f
f
rtabmap::util3d::generateWords3DMono
std::map< int, cv::Point3f > RTABMAP_EXP generateWords3DMono(const std::map< int, cv::KeyPoint > &kpts, const std::map< int, cv::KeyPoint > &previousKpts, const CameraModel &cameraModel, Transform &cameraTransform, float ransacReprojThreshold=3.0f, float ransacConfidence=0.99f, const std::map< int, cv::Point3f > &refGuess3D=std::map< int, cv::Point3f >(), double *variance=0, std::vector< int > *matchesOut=0)
Definition: util3d_features.cpp:209
x
x
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cv::Mat findEssentialMat(InputArray _points1, InputArray _points2, InputArray _cameraMatrix, int method, double prob, double threshold, OutputArray _mask)
Definition: five-point.cpp:405
util3d_motion_estimation.h
glm::scale
GLM_FUNC_DECL detail::tmat4x4< T, P > scale(detail::tmat4x4< T, P > const &m, detail::tvec3< T, P > const &v)
glm::all
GLM_FUNC_DECL bool all(vecType< bool, P > const &v)
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double fx() const
Definition: CameraModel.h:102
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Basic mathematics functions.
rtabmap::Transform::getNorm
float getNorm() const
Definition: Transform.cpp:273
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Some conversion functions.
util3d_correspondences.h
UASSERT
#define UASSERT(condition)
UStl.h
Wrappers of STL for convenient functions.
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pcl::PointXYZ RTABMAP_EXP projectDepthTo3D(const cv::Mat &depthImage, float x, float y, float cx, float cy, float fx, float fy, bool smoothing, float depthErrorRatio=0.02f)
Definition: util3d.cpp:214
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std::multimap< int, cv::KeyPoint > RTABMAP_EXP aggregate(const std::list< int > &wordIds, const std::vector< cv::KeyPoint > &keypoints)
Definition: util3d_features.cpp:414
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#define UASSERT_MSG(condition, msg_str)
Definition: ULogger.h:67
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int recoverPose(InputArray E, InputArray _points1, InputArray _points2, InputArray _cameraMatrix, OutputArray _R, OutputArray _t, double distanceThresh, InputOutputArray _mask, OutputArray triangulatedPoints)
Definition: five-point.cpp:477
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cv::Mat K() const
Definition: CameraModel.h:110
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std::string prettyPrint() const
Definition: Transform.cpp:316
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static int findPairs(const std::map< int, T > &wordsA, const std::map< int, T > &wordsB, std::list< std::pair< int, std::pair< T, T > > > &pairs, bool ignoreNegativeIds=true)
Definition: EpipolarGeometry.h:96
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const Transform & localTransform() const
Definition: CameraModel.h:116
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rtabmap::util3d::generateKeypoints3DDisparity
std::vector< cv::Point3f > RTABMAP_EXP generateKeypoints3DDisparity(const std::vector< cv::KeyPoint > &keypoints, const cv::Mat &disparity, const StereoCameraModel &stereoCameraModel, float minDepth=0, float maxDepth=0)
Definition: util3d_features.cpp:122
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bool isNull() const
Definition: Transform.cpp:107
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const Transform & localTransform() const
Definition: StereoCameraModel.h:119
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#define UDEBUG(...)
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T uNormSquared(const std::vector< T > &v)
Definition: UMath.h:560
ULogger.h
ULogger class and convenient macros.
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#define UWARN(...)
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Definition: trace.py:4
util3d_features.h
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std::vector< cv::Point3f > RTABMAP_EXP generateKeypoints3DDepth(const std::vector< cv::KeyPoint > &keypoints, const cv::Mat &depth, const CameraModel &cameraModel, float minDepth=0, float maxDepth=0)
Definition: util3d_features.cpp:54
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Definition: UConversion.cpp:365
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Definition: CameraModel.h:87
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void RTABMAP_EXP findCorrespondences(const std::multimap< int, cv::KeyPoint > &wordsA, const std::multimap< int, cv::KeyPoint > &wordsB, std::list< std::pair< cv::Point2f, cv::Point2f > > &pairs)
Definition: util3d_correspondences.cpp:300
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bool isIdentity() const
Definition: Transform.cpp:136
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Definition: Transform.cpp:178
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std::vector< cv::Point3f > RTABMAP_EXP generateKeypoints3DStereo(const std::vector< cv::Point2f > &leftCorners, const std::vector< cv::Point2f > &rightCorners, const StereoCameraModel &model, const std::vector< unsigned char > &mask=std::vector< unsigned char >(), float minDepth=0, float maxDepth=0)
Definition: util3d_features.cpp:158
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const CameraModel & left() const
Definition: StereoCameraModel.h:122

rtabmap
Author(s): Mathieu Labbe
autogenerated on Mon Jan 23 2023 03:38:58