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gtsam/gtsam_unstable/slam/SmartStereoProjectionFactor.h

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file SmartStereoProjectionFactor.h
* @brief Base class to create smart factors on poses or cameras
* @author Luca Carlone
* @author Zsolt Kira
* @author Frank Dellaert
*/
#pragma once
#include <gtsam/slam/SmartFactorBase.h>
#include <gtsam/geometry/triangulation.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/geometry/StereoCamera.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/slam/dataset.h>
#include <boost/optional.hpp>
#include <boost/make_shared.hpp>
#include <vector>
namespace gtsam {
/**
* Structure for storing some state memory, used to speed up optimization
* @addtogroup SLAM
*/
class SmartStereoProjectionFactorState {
protected:
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
SmartStereoProjectionFactorState() {
}
// Hessian representation (after Schur complement)
bool calculatedHessian;
Matrix H;
Vector gs_vector;
std::vector<Matrix> Gs;
std::vector<Vector> gs;
double f;
};
enum LinearizationMode {
HESSIAN, JACOBIAN_SVD, JACOBIAN_Q
};
/**
* SmartStereoProjectionFactor: triangulates point
*/
template<class CALIBRATION, size_t D>
class SmartStereoProjectionFactor: public SmartFactorBase<StereoCamera, D> {
protected:
// Some triangulation parameters
const double rankTolerance_; ///< threshold to decide whether triangulation is degenerate_
const double retriangulationThreshold_; ///< threshold to decide whether to re-triangulate
mutable std::vector<Pose3> cameraPosesTriangulation_; ///< current triangulation poses
const bool manageDegeneracy_; ///< if set to true will use the rotation-only version for degenerate cases
const bool enableEPI_; ///< if set to true, will refine triangulation using LM
const double linearizationThreshold_; ///< threshold to decide whether to re-linearize
mutable std::vector<Pose3> cameraPosesLinearization_; ///< current linearization poses
mutable Point3 point_; ///< Current estimate of the 3D point
mutable bool degenerate_;
mutable bool cheiralityException_;
// verbosity handling for Cheirality Exceptions
const bool throwCheirality_; ///< If true, rethrows Cheirality exceptions (default: false)
const bool verboseCheirality_; ///< If true, prints text for Cheirality exceptions (default: false)
boost::shared_ptr<SmartStereoProjectionFactorState> state_;
/// shorthand for smart projection factor state variable
typedef boost::shared_ptr<SmartStereoProjectionFactorState> SmartFactorStatePtr;
/// shorthand for base class type
typedef SmartFactorBase<StereoCamera, D> Base;
double landmarkDistanceThreshold_; // if the landmark is triangulated at a
// distance larger than that the factor is considered degenerate
double dynamicOutlierRejectionThreshold_; // if this is nonnegative the factor will check if the
// average reprojection error is smaller than this threshold after triangulation,
// and the factor is disregarded if the error is large
/// shorthand for this class
typedef SmartStereoProjectionFactor<CALIBRATION, D> This;
enum {ZDim = 3}; ///< Dimension trait of measurement type
public:
/// shorthand for a smart pointer to a factor
typedef boost::shared_ptr<This> shared_ptr;
/// shorthand for a StereoCamera // TODO: Get rid of this?
typedef StereoCamera Camera;
/// Vector of cameras
typedef CameraSet<Camera> Cameras;
/**
* Constructor
* @param rankTol tolerance used to check if point triangulation is degenerate
* @param linThreshold threshold on relative pose changes used to decide whether to relinearize (selective relinearization)
* @param manageDegeneracy is true, in presence of degenerate triangulation, the factor is converted to a rotation-only constraint,
* otherwise the factor is simply neglected
* @param enableEPI if set to true linear triangulation is refined with embedded LM iterations
* @param body_P_sensor is the transform from body to sensor frame (default identity)
*/
SmartStereoProjectionFactor(const double rankTol, const double linThreshold,
const bool manageDegeneracy, const bool enableEPI,
boost::optional<Pose3> body_P_sensor = boost::none,
double landmarkDistanceThreshold = 1e10,
double dynamicOutlierRejectionThreshold = -1,
SmartFactorStatePtr state = SmartFactorStatePtr(new SmartStereoProjectionFactorState())) :
Base(body_P_sensor), rankTolerance_(rankTol), retriangulationThreshold_(
1e-5), manageDegeneracy_(manageDegeneracy), enableEPI_(enableEPI), linearizationThreshold_(
linThreshold), degenerate_(false), cheiralityException_(false), throwCheirality_(
false), verboseCheirality_(false), state_(state),
landmarkDistanceThreshold_(landmarkDistanceThreshold),
dynamicOutlierRejectionThreshold_(dynamicOutlierRejectionThreshold) {
}
/** Virtual destructor */
virtual ~SmartStereoProjectionFactor() {
}
/**
* print
* @param s optional string naming the factor
* @param keyFormatter optional formatter useful for printing Symbols
*/
void print(const std::string& s = "", const KeyFormatter& keyFormatter =
DefaultKeyFormatter) const {
std::cout << s << "SmartStereoProjectionFactor, z = \n";
std::cout << "rankTolerance_ = " << rankTolerance_ << std::endl;
std::cout << "degenerate_ = " << degenerate_ << std::endl;
std::cout << "cheiralityException_ = " << cheiralityException_ << std::endl;
std::cout << "linearizationThreshold_ = " << linearizationThreshold_ << std::endl;
Base::print("", keyFormatter);
}
/// Check if the new linearization point_ is the same as the one used for previous triangulation
bool decideIfTriangulate(const Cameras& cameras) const {
// several calls to linearize will be done from the same linearization point_, hence it is not needed to re-triangulate
// Note that this is not yet "selecting linearization", that will come later, and we only check if the
// current linearization is the "same" (up to tolerance) w.r.t. the last time we triangulated the point_
size_t m = cameras.size();
bool retriangulate = false;
// if we do not have a previous linearization point_ or the new linearization point_ includes more poses
if (cameraPosesTriangulation_.empty()
|| cameras.size() != cameraPosesTriangulation_.size())
retriangulate = true;
if (!retriangulate) {
for (size_t i = 0; i < cameras.size(); i++) {
if (!cameras[i].pose().equals(cameraPosesTriangulation_[i],
retriangulationThreshold_)) {
retriangulate = true; // at least two poses are different, hence we retriangulate
break;
}
}
}
if (retriangulate) { // we store the current poses used for triangulation
cameraPosesTriangulation_.clear();
cameraPosesTriangulation_.reserve(m);
for (size_t i = 0; i < m; i++)
// cameraPosesTriangulation_[i] = cameras[i].pose();
cameraPosesTriangulation_.push_back(cameras[i].pose());
}
return retriangulate; // if we arrive to this point_ all poses are the same and we don't need re-triangulation
}
/// This function checks if the new linearization point_ is 'close' to the previous one used for linearization
bool decideIfLinearize(const Cameras& cameras) const {
// "selective linearization"
// The function evaluates how close are the old and the new poses, transformed in the ref frame of the first pose
// (we only care about the "rigidity" of the poses, not about their absolute pose)
if (this->linearizationThreshold_ < 0) //by convention if linearizationThreshold is negative we always relinearize
return true;
// if we do not have a previous linearization point_ or the new linearization point_ includes more poses
if (cameraPosesLinearization_.empty()
|| (cameras.size() != cameraPosesLinearization_.size()))
return true;
Pose3 firstCameraPose, firstCameraPoseOld;
for (size_t i = 0; i < cameras.size(); i++) {
if (i == 0) { // we store the initial pose, this is useful for selective re-linearization
firstCameraPose = cameras[i].pose();
firstCameraPoseOld = cameraPosesLinearization_[i];
continue;
}
// we compare the poses in the frame of the first pose
Pose3 localCameraPose = firstCameraPose.between(cameras[i].pose());
Pose3 localCameraPoseOld = firstCameraPoseOld.between(
cameraPosesLinearization_[i]);
if (!localCameraPose.equals(localCameraPoseOld,
this->linearizationThreshold_))
return true; // at least two "relative" poses are different, hence we re-linearize
}
return false; // if we arrive to this point_ all poses are the same and we don't need re-linearize
}
/// triangulateSafe
size_t triangulateSafe(const Values& values) const {
return triangulateSafe(this->cameras(values));
}
/// triangulateSafe
size_t triangulateSafe(const Cameras& cameras) const {
size_t m = cameras.size();
if (m < 2) { // if we have a single pose the corresponding factor is uninformative
degenerate_ = true;
return m;
}
bool retriangulate = decideIfTriangulate(cameras);
if (retriangulate) {
// We triangulate the 3D position of the landmark
try {
// std::cout << "triangulatePoint3 i \n" << rankTolerance << std::endl;
//TODO: Chris will replace this with something else for stereo
// point_ = triangulatePoint3<CALIBRATION>(cameras, this->measured_,
// rankTolerance_, enableEPI_);
// // // Temporary hack to use monocular triangulation
std::vector<Point2> mono_measurements;
BOOST_FOREACH(const StereoPoint2& sp, this->measured_) {
mono_measurements.push_back(sp.point2());
}
std::vector<PinholeCamera<Cal3_S2> > mono_cameras;
BOOST_FOREACH(const Camera& camera, cameras) {
const Pose3& pose = camera.pose();
const Cal3_S2& K = camera.calibration()->calibration();
mono_cameras.push_back(PinholeCamera<Cal3_S2>(pose, K));
}
point_ = triangulatePoint3<PinholeCamera<Cal3_S2> >(mono_cameras, mono_measurements,
rankTolerance_, enableEPI_);
// // // End temporary hack
// FIXME: temporary: triangulation using only first camera
// const StereoPoint2& z0 = this->measured_.at(0);
// point_ = cameras[0].backproject(z0);
degenerate_ = false;
cheiralityException_ = false;
// Check landmark distance and reprojection errors to avoid outliers
double totalReprojError = 0.0;
size_t i=0;
BOOST_FOREACH(const Camera& camera, cameras) {
Point3 cameraTranslation = camera.pose().translation();
// we discard smart factors corresponding to points that are far away
if(cameraTranslation.distance(point_) > landmarkDistanceThreshold_){
degenerate_ = true;
break;
}
const StereoPoint2& zi = this->measured_.at(i);
try {
StereoPoint2 reprojectionError(camera.project(point_) - zi);
totalReprojError += reprojectionError.vector().norm();
} catch (CheiralityException) {
cheiralityException_ = true;
}
i += 1;
}
//std::cout << "totalReprojError error: " << totalReprojError << std::endl;
// we discard smart factors that have large reprojection error
if(dynamicOutlierRejectionThreshold_ > 0 &&
totalReprojError/m > dynamicOutlierRejectionThreshold_)
degenerate_ = true;
} catch (TriangulationUnderconstrainedException&) {
// if TriangulationUnderconstrainedException can be
// 1) There is a single pose for triangulation - this should not happen because we checked the number of poses before
// 2) The rank of the matrix used for triangulation is < 3: rotation-only, parallel cameras (or motion towards the landmark)
// in the second case we want to use a rotation-only smart factor
degenerate_ = true;
cheiralityException_ = false;
} catch (TriangulationCheiralityException&) {
// point is behind one of the cameras: can be the case of close-to-parallel cameras or may depend on outliers
// we manage this case by either discarding the smart factor, or imposing a rotation-only constraint
cheiralityException_ = true;
}
}
return m;
}
/// triangulate
bool triangulateForLinearize(const Cameras& cameras) const {
bool isDebug = false;
size_t nrCameras = this->triangulateSafe(cameras);
if (nrCameras < 2
|| (!this->manageDegeneracy_
&& (this->cheiralityException_ || this->degenerate_))) {
if (isDebug) {
std::cout << "createImplicitSchurFactor: degenerate configuration"
<< std::endl;
}
return false;
} else {
// instead, if we want to manage the exception..
if (this->cheiralityException_ || this->degenerate_) { // if we want to manage the exceptions with rotation-only factors
this->degenerate_ = true;
}
return true;
}
}
/// linearize returns a Hessianfactor that is an approximation of error(p)
boost::shared_ptr<RegularHessianFactor<D> > createHessianFactor(
const Cameras& cameras, const double lambda = 0.0) const {
bool isDebug = false;
size_t numKeys = this->keys_.size();
// Create structures for Hessian Factors
std::vector < Key > js;
std::vector < Matrix > Gs(numKeys * (numKeys + 1) / 2);
std::vector < Vector > gs(numKeys);
if (this->measured_.size() != cameras.size()) {
std::cout
<< "SmartProjectionHessianFactor: this->measured_.size() inconsistent with input"
<< std::endl;
exit(1);
}
this->triangulateSafe(cameras);
if (isDebug) std::cout << "point_ = " << point_ << std::endl;
if (numKeys < 2
|| (!this->manageDegeneracy_
&& (this->cheiralityException_ || this->degenerate_))) {
if (isDebug) std::cout << "In linearize: exception" << std::endl;
BOOST_FOREACH(Matrix& m, Gs)
m = zeros(D, D);
BOOST_FOREACH(Vector& v, gs)
v = zero(D);
return boost::make_shared<RegularHessianFactor<D> >(this->keys_, Gs, gs,
0.0);
}
// instead, if we want to manage the exception..
if (this->cheiralityException_ || this->degenerate_) { // if we want to manage the exceptions with rotation-only factors
this->degenerate_ = true;
if (isDebug) std::cout << "degenerate_ = true" << std::endl;
}
bool doLinearize = this->decideIfLinearize(cameras);
if (isDebug) std::cout << "doLinearize = " << doLinearize << std::endl;
if (this->linearizationThreshold_ >= 0 && doLinearize) // if we apply selective relinearization and we need to relinearize
for (size_t i = 0; i < cameras.size(); i++)
this->cameraPosesLinearization_[i] = cameras[i].pose();
if (!doLinearize) { // return the previous Hessian factor
std::cout << "=============================" << std::endl;
std::cout << "doLinearize " << doLinearize << std::endl;
std::cout << "this->linearizationThreshold_ "
<< this->linearizationThreshold_ << std::endl;
std::cout << "this->degenerate_ " << this->degenerate_ << std::endl;
std::cout
<< "something wrong in SmartProjectionHessianFactor: selective relinearization should be disabled"
<< std::endl;
exit(1);
return boost::make_shared<RegularHessianFactor<D> >(this->keys_,
this->state_->Gs, this->state_->gs, this->state_->f);
}
// ==================================================================
Matrix F, E;
Vector b;
double f = computeJacobians(F, E, b, cameras);
// Schur complement trick
// Frank says: should be possible to do this more efficiently?
// And we care, as in grouped factors this is called repeatedly
Matrix H(D * numKeys, D * numKeys);
Vector gs_vector;
Matrix3 P = Base::PointCov(E,lambda);
H.noalias() = F.transpose() * (F - (E * (P * (E.transpose() * F))));
gs_vector.noalias() = F.transpose() * (b - (E * (P * (E.transpose() * b))));
if (isDebug) std::cout << "gs_vector size " << gs_vector.size() << std::endl;
if (isDebug) std::cout << "H:\n" << H << std::endl;
// Populate Gs and gs
int GsCount2 = 0;
for (DenseIndex i1 = 0; i1 < (DenseIndex)numKeys; i1++) { // for each camera
DenseIndex i1D = i1 * D;
gs.at(i1) = gs_vector.segment < D > (i1D);
for (DenseIndex i2 = 0; i2 < (DenseIndex)numKeys; i2++) {
if (i2 >= i1) {
Gs.at(GsCount2) = H.block < D, D > (i1D, i2 * D);
GsCount2++;
}
}
}
// ==================================================================
if (this->linearizationThreshold_ >= 0) { // if we do not use selective relinearization we don't need to store these variables
this->state_->Gs = Gs;
this->state_->gs = gs;
this->state_->f = f;
}
return boost::make_shared<RegularHessianFactor<D> >(this->keys_, Gs, gs, f);
}
// // create factor
// boost::shared_ptr<ImplicitSchurFactor<D> > createImplicitSchurFactor(
// const Cameras& cameras, double lambda) const {
// if (triangulateForLinearize(cameras))
// return Base::createImplicitSchurFactor(cameras, point_, lambda);
// else
// return boost::shared_ptr<ImplicitSchurFactor<D> >();
// }
//
// /// create factor
// boost::shared_ptr<JacobianFactorQ<D> > createJacobianQFactor(
// const Cameras& cameras, double lambda) const {
// if (triangulateForLinearize(cameras))
// return Base::createJacobianQFactor(cameras, point_, lambda);
// else
// return boost::make_shared< JacobianFactorQ<D> >(this->keys_);
// }
//
// /// Create a factor, takes values
// boost::shared_ptr<JacobianFactorQ<D> > createJacobianQFactor(
// const Values& values, double lambda) const {
// Cameras myCameras;
// // TODO triangulate twice ??
// bool nonDegenerate = computeCamerasAndTriangulate(values, myCameras);
// if (nonDegenerate)
// return createJacobianQFactor(myCameras, lambda);
// else
// return boost::make_shared< JacobianFactorQ<D> >(this->keys_);
// }
//
/// different (faster) way to compute Jacobian factor
boost::shared_ptr< JacobianFactor > createJacobianSVDFactor(const Cameras& cameras,
double lambda) const {
if (triangulateForLinearize(cameras))
return Base::createJacobianSVDFactor(cameras, point_, lambda);
else
return boost::make_shared< JacobianFactorSVD<D, ZDim> >(this->keys_);
}
/// Returns true if nonDegenerate
bool computeCamerasAndTriangulate(const Values& values,
Cameras& myCameras) const {
Values valuesFactor;
// Select only the cameras
BOOST_FOREACH(const Key key, this->keys_)
valuesFactor.insert(key, values.at(key));
myCameras = this->cameras(valuesFactor);
size_t nrCameras = this->triangulateSafe(myCameras);
if (nrCameras < 2
|| (!this->manageDegeneracy_
&& (this->cheiralityException_ || this->degenerate_)))
return false;
// instead, if we want to manage the exception..
if (this->cheiralityException_ || this->degenerate_) // if we want to manage the exceptions with rotation-only factors
this->degenerate_ = true;
if (this->degenerate_) {
std::cout << "SmartStereoProjectionFactor: this is not ready" << std::endl;
std::cout << "this->cheiralityException_ " << this->cheiralityException_
<< std::endl;
std::cout << "this->degenerate_ " << this->degenerate_ << std::endl;
}
return true;
}
/// Assumes non-degenerate !
void computeEP(Matrix& E, Matrix& PointCov, const Cameras& cameras) const {
Base::computeEP(E, PointCov, cameras, point_);
}
/// Takes values
bool computeEP(Matrix& E, Matrix& PointCov, const Values& values) const {
Cameras myCameras;
bool nonDegenerate = computeCamerasAndTriangulate(values, myCameras);
if (nonDegenerate)
computeEP(E, PointCov, myCameras);
return nonDegenerate;
}
/// Version that takes values, and creates the point
bool computeJacobians(std::vector<typename Base::KeyMatrix2D>& Fblocks,
Matrix& E, Vector& b, const Values& values) const {
Cameras myCameras;
bool nonDegenerate = computeCamerasAndTriangulate(values, myCameras);
if (nonDegenerate)
computeJacobians(Fblocks, E, b, myCameras, 0.0);
return nonDegenerate;
}
/// Compute F, E only (called below in both vanilla and SVD versions)
/// Assumes the point has been computed
/// Note E can be 2m*3 or 2m*2, in case point is degenerate
double computeJacobians(std::vector<typename Base::KeyMatrix2D>& Fblocks,
Matrix& E, Vector& b, const Cameras& cameras) const {
if (this->degenerate_) {
throw("FIXME: computeJacobians degenerate case commented out!");
// std::cout << "manage degeneracy " << manageDegeneracy_ << std::endl;
// std::cout << "point " << point_ << std::endl;
// std::cout
// << "SmartStereoProjectionFactor: Management of degeneracy is disabled - not ready to be used"
// << std::endl;
// if (D > 6) {
// std::cout
// << "Management of degeneracy is not yet ready when one also optimizes for the calibration "
// << std::endl;
// }
//
// int numKeys = this->keys_.size();
// E = zeros(2 * numKeys, 2);
// b = zero(2 * numKeys);
// double f = 0;
// for (size_t i = 0; i < this->measured_.size(); i++) {
// if (i == 0) { // first pose
// this->point_ = cameras[i].backprojectPointAtInfinity(
// this->measured_.at(i));
// // 3D parametrization of point at infinity: [px py 1]
// }
// Matrix Fi, Ei;
// Vector bi = -(cameras[i].projectPointAtInfinity(this->point_, Fi, Ei)
// - this->measured_.at(i)).vector();
//
// this->noise_.at(i)->WhitenSystem(Fi, Ei, bi);
// f += bi.squaredNorm();
// Fblocks.push_back(typename Base::KeyMatrix2D(this->keys_[i], Fi));
// E.block < 2, 2 > (2 * i, 0) = Ei;
// subInsert(b, bi, 2 * i);
// }
// return f;
} else {
// nondegenerate: just return Base version
return Base::computeJacobians(Fblocks, E, b, cameras, point_);
} // end else
}
// /// Version that computes PointCov, with optional lambda parameter
// double computeJacobians(std::vector<typename Base::KeyMatrix2D>& Fblocks,
// Matrix& E, Matrix& PointCov, Vector& b, const Cameras& cameras,
// const double lambda = 0.0) const {
//
// double f = computeJacobians(Fblocks, E, b, cameras);
//
// // Point covariance inv(E'*E)
// PointCov.noalias() = (E.transpose() * E + lambda * eye(E.cols())).inverse();
//
// return f;
// }
//
// /// takes values
// bool computeJacobiansSVD(std::vector<typename Base::KeyMatrix2D>& Fblocks,
// Matrix& Enull, Vector& b, const Values& values) const {
// typename Base::Cameras myCameras;
// double good = computeCamerasAndTriangulate(values, myCameras);
// if (good)
// computeJacobiansSVD(Fblocks, Enull, b, myCameras);
// return true;
// }
//
// /// SVD version
// double computeJacobiansSVD(std::vector<typename Base::KeyMatrix2D>& Fblocks,
// Matrix& Enull, Vector& b, const Cameras& cameras) const {
// return Base::computeJacobiansSVD(Fblocks, Enull, b, cameras, point_);
// }
//
// /// Returns Matrix, TODO: maybe should not exist -> not sparse !
// // TODO should there be a lambda?
// double computeJacobiansSVD(Matrix& F, Matrix& Enull, Vector& b,
// const Cameras& cameras) const {
// return Base::computeJacobiansSVD(F, Enull, b, cameras, point_);
// }
/// Returns Matrix, TODO: maybe should not exist -> not sparse !
double computeJacobians(Matrix& F, Matrix& E, Vector& b,
const Cameras& cameras) const {
return Base::computeJacobians(F, E, b, cameras, point_);
}
/// Calculate vector of re-projection errors, before applying noise model
/// Assumes triangulation was done and degeneracy handled
Vector reprojectionError(const Cameras& cameras) const {
return Base::reprojectionError(cameras, point_);
}
/// Calculate vector of re-projection errors, before applying noise model
Vector reprojectionError(const Values& values) const {
Cameras myCameras;
bool nonDegenerate = computeCamerasAndTriangulate(values, myCameras);
if (nonDegenerate)
return reprojectionError(myCameras);
else
return zero(myCameras.size() * 3);
}
/**
* Calculate the error of the factor.
* This is the log-likelihood, e.g. \f$ 0.5(h(x)-z)^2/\sigma^2 \f$ in case of Gaussian.
* In this class, we take the raw prediction error \f$ h(x)-z \f$, ask the noise model
* to transform it to \f$ (h(x)-z)^2/\sigma^2 \f$, and then multiply by 0.5.
*/
double totalReprojectionError(const Cameras& cameras,
boost::optional<Point3> externalPoint = boost::none) const {
size_t nrCameras;
if (externalPoint) {
nrCameras = this->keys_.size();
point_ = *externalPoint;
degenerate_ = false;
cheiralityException_ = false;
} else {
nrCameras = this->triangulateSafe(cameras);
}
if (nrCameras < 2
|| (!this->manageDegeneracy_
&& (this->cheiralityException_ || this->degenerate_))) {
// if we don't want to manage the exceptions we discard the factor
// std::cout << "In error evaluation: exception" << std::endl;
return 0.0;
}
if (this->cheiralityException_) { // if we want to manage the exceptions with rotation-only factors
std::cout
<< "SmartProjectionHessianFactor: cheirality exception (this should not happen if CheiralityException is disabled)!"
<< std::endl;
this->degenerate_ = true;
}
if (this->degenerate_) {
return 0.0; // TODO: this maybe should be zero?
// std::cout
// << "SmartProjectionHessianFactor: trying to manage degeneracy (this should not happen is manageDegeneracy is disabled)!"
// << std::endl;
// size_t i = 0;
// double overallError = 0;
// BOOST_FOREACH(const Camera& camera, cameras) {
// const StereoPoint2& zi = this->measured_.at(i);
// if (i == 0) // first pose
// this->point_ = camera.backprojectPointAtInfinity(zi); // 3D parametrization of point at infinity
// StereoPoint2 reprojectionError(
// camera.projectPointAtInfinity(this->point_) - zi);
// overallError += 0.5
// * this->noise_.at(i)->distance(reprojectionError.vector());
// i += 1;
// }
// return overallError;
} else {
// Just use version in base class
return Base::totalReprojectionError(cameras, point_);
}
}
/// Cameras are computed in derived class
virtual Cameras cameras(const Values& values) const = 0;
/** return the landmark */
boost::optional<Point3> point() const {
return point_;
}
/** COMPUTE the landmark */
boost::optional<Point3> point(const Values& values) const {
triangulateSafe(values);
return point_;
}
/** return the degenerate state */
inline bool isDegenerate() const {
return (cheiralityException_ || degenerate_);
}
/** return the cheirality status flag */
inline bool isPointBehindCamera() const {
return cheiralityException_;
}
/** return chirality verbosity */
inline bool verboseCheirality() const {
return verboseCheirality_;
}
/** return flag for throwing cheirality exceptions */
inline bool throwCheirality() const {
return throwCheirality_;
}
private:
/// Serialization function
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
ar & BOOST_SERIALIZATION_NVP(throwCheirality_);
ar & BOOST_SERIALIZATION_NVP(verboseCheirality_);
}
};
/// traits
template<class CALIBRATION, size_t D>
struct traits<SmartStereoProjectionFactor<CALIBRATION, D> > :
public Testable<SmartStereoProjectionFactor<CALIBRATION, D> > {
};
} // \ namespace gtsam
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