use of boofcv.abst.geo.EstimateNofPnP in project BoofCV by lessthanoptimal.
the class TestPnPStereoEstimator method perfectData.
private void perfectData(int numExtra) {
EstimateNofPnP pnp = FactoryMultiView.computePnP_N(EnumPNP.P3P_FINSTERWALDER, -1);
DistanceModelMonoPixels<Se3_F64, Point2D3D> distanceMono = new PnPDistanceReprojectionSq();
PnPStereoEstimator alg = new PnPStereoEstimator(pnp, distanceMono, numExtra);
Se3_F64 expected = new Se3_F64();
expected.getR().set(ConvertRotation3D_F64.eulerToMatrix(EulerType.XYZ, 0.05, -0.03, 0.02, null));
expected.getT().set(0.2, -0.1, 0.01);
generateScene(alg.getMinimumPoints(), expected, false);
Se3_F64 found = new Se3_F64();
alg.setLeftToRight(leftToRight);
assertTrue(alg.process(pointPose, found));
// found.print();
// expected.print();
assertTrue(MatrixFeatures_DDRM.isIdentical(expected.getR(), found.getR(), 1e-8));
assertTrue(found.getT().isIdentical(expected.getT(), 1e-8));
}
use of boofcv.abst.geo.EstimateNofPnP in project BoofCV by lessthanoptimal.
the class FactoryVisualOdometry method stereoQuadPnP.
/**
* Stereo visual odometry which uses the two most recent stereo observations (total of four views) to estimate
* motion.
*
* @see VisOdomQuadPnP
*
* @param inlierPixelTol Pixel tolerance for RANSAC inliers - Euclidean distance
* @param epipolarPixelTol Feature association tolerance in pixels.
* @param maxDistanceF2F Maximum allowed distance between two features in pixels
* @param maxAssociationError Maxium error between two features when associating.
* @param ransacIterations Number of iterations RANSAC will perform
* @param refineIterations Number of refinement iterations
* @param detector Which feature detector to use
* @param imageType Type of input image
*/
public static <T extends ImageGray<T>, Desc extends TupleDesc> StereoVisualOdometry<T> stereoQuadPnP(double inlierPixelTol, double epipolarPixelTol, double maxDistanceF2F, double maxAssociationError, int ransacIterations, int refineIterations, DetectDescribeMulti<T, Desc> detector, Class<T> imageType) {
EstimateNofPnP pnp = FactoryMultiView.computePnP_N(EnumPNP.P3P_FINSTERWALDER, -1);
DistanceModelMonoPixels<Se3_F64, Point2D3D> distanceMono = new PnPDistanceReprojectionSq();
PnPStereoDistanceReprojectionSq distanceStereo = new PnPStereoDistanceReprojectionSq();
PnPStereoEstimator pnpStereo = new PnPStereoEstimator(pnp, distanceMono, 0);
ModelManagerSe3_F64 manager = new ModelManagerSe3_F64();
EstimatorToGenerator<Se3_F64, Stereo2D3D> generator = new EstimatorToGenerator<>(pnpStereo);
// euclidean error squared from left + right images
double ransacTOL = 2 * inlierPixelTol * inlierPixelTol;
ModelMatcher<Se3_F64, Stereo2D3D> motion = new Ransac<>(2323, manager, generator, distanceStereo, ransacIterations, ransacTOL);
RefinePnPStereo refinePnP = null;
if (refineIterations > 0) {
refinePnP = new PnPStereoRefineRodrigues(1e-12, refineIterations);
}
Class<Desc> descType = detector.getDescriptionType();
ScoreAssociation<Desc> scorer = FactoryAssociation.defaultScore(descType);
AssociateDescription2D<Desc> assocSame;
if (maxDistanceF2F > 0)
assocSame = new AssociateMaxDistanceNaive<>(scorer, true, maxAssociationError, maxDistanceF2F);
else
assocSame = new AssociateDescTo2D<>(FactoryAssociation.greedy(scorer, maxAssociationError, true));
AssociateStereo2D<Desc> associateStereo = new AssociateStereo2D<>(scorer, epipolarPixelTol, descType);
TriangulateTwoViewsCalibrated triangulate = FactoryMultiView.triangulateTwoGeometric();
associateStereo.setThreshold(maxAssociationError);
VisOdomQuadPnP<T, Desc> alg = new VisOdomQuadPnP<>(detector, assocSame, associateStereo, triangulate, motion, refinePnP);
return new WrapVisOdomQuadPnP<>(alg, refinePnP, associateStereo, distanceStereo, distanceMono, imageType);
}
use of boofcv.abst.geo.EstimateNofPnP in project BoofCV by lessthanoptimal.
the class FactoryVisualOdometry method stereoDualTrackerPnP.
/**
* Creates a stereo visual odometry algorithm that independently tracks features in left and right camera.
*
* @see VisOdomDualTrackPnP
*
* @param thresholdAdd When the number of inliers is below this number new features are detected
* @param thresholdRetire When a feature has not been in the inlier list for this many ticks it is dropped
* @param inlierPixelTol Tolerance in pixels for defining an inlier during robust model matching. Typically 1.5
* @param epipolarPixelTol Tolerance in pixels for enforcing the epipolar constraint
* @param ransacIterations Number of iterations performed by RANSAC. Try 300 or more.
* @param refineIterations Number of iterations done during non-linear optimization. Try 50 or more.
* @param trackerLeft Tracker used for left camera
* @param trackerRight Tracker used for right camera
* @param imageType Type of image being processed
* @return Stereo visual odometry algorithm.
*/
public static <T extends ImageGray<T>, Desc extends TupleDesc> StereoVisualOdometry<T> stereoDualTrackerPnP(int thresholdAdd, int thresholdRetire, double inlierPixelTol, double epipolarPixelTol, int ransacIterations, int refineIterations, PointTracker<T> trackerLeft, PointTracker<T> trackerRight, DescribeRegionPoint<T, Desc> descriptor, Class<T> imageType) {
EstimateNofPnP pnp = FactoryMultiView.computePnP_N(EnumPNP.P3P_FINSTERWALDER, -1);
DistanceModelMonoPixels<Se3_F64, Point2D3D> distanceMono = new PnPDistanceReprojectionSq();
PnPStereoDistanceReprojectionSq distanceStereo = new PnPStereoDistanceReprojectionSq();
PnPStereoEstimator pnpStereo = new PnPStereoEstimator(pnp, distanceMono, 0);
ModelManagerSe3_F64 manager = new ModelManagerSe3_F64();
EstimatorToGenerator<Se3_F64, Stereo2D3D> generator = new EstimatorToGenerator<>(pnpStereo);
// Pixel tolerance for RANSAC inliers - euclidean error squared from left + right images
double ransacTOL = 2 * inlierPixelTol * inlierPixelTol;
ModelMatcher<Se3_F64, Stereo2D3D> motion = new Ransac<>(2323, manager, generator, distanceStereo, ransacIterations, ransacTOL);
RefinePnPStereo refinePnP = null;
Class<Desc> descType = descriptor.getDescriptionType();
ScoreAssociation<Desc> scorer = FactoryAssociation.defaultScore(descType);
AssociateStereo2D<Desc> associateStereo = new AssociateStereo2D<>(scorer, epipolarPixelTol, descType);
// need to make sure associations are unique
AssociateDescription2D<Desc> associateUnique = associateStereo;
if (!associateStereo.uniqueDestination() || !associateStereo.uniqueSource()) {
associateUnique = new EnforceUniqueByScore.Describe2D<>(associateStereo, true, true);
}
if (refineIterations > 0) {
refinePnP = new PnPStereoRefineRodrigues(1e-12, refineIterations);
}
TriangulateTwoViewsCalibrated triangulate = FactoryMultiView.triangulateTwoGeometric();
VisOdomDualTrackPnP<T, Desc> alg = new VisOdomDualTrackPnP<>(thresholdAdd, thresholdRetire, epipolarPixelTol, trackerLeft, trackerRight, descriptor, associateUnique, triangulate, motion, refinePnP);
return new WrapVisOdomDualTrackPnP<>(pnpStereo, distanceMono, distanceStereo, associateStereo, alg, refinePnP, imageType);
}
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