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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 SmartProjectionPoseFactorRollingShutter.h
* @brief Smart projection factor on poses modeling rolling shutter effect with given readout time
* @author Luca Carlone
*/
#pragma once
#include <gtsam/slam/SmartProjectionFactor.h>
#include <gtsam/geometry/CameraSet.h>
namespace gtsam {
/**
*
* @addtogroup SLAM
*
* If you are using the factor, please cite:
* L. Carlone, Z. Kira, C. Beall, V. Indelman, F. Dellaert,
* Eliminating conditionally independent sets in factor graphs:
* a unifying perspective based on smart factors,
* Int. Conf. on Robotics and Automation (ICRA), 2014.
*/
/**
* This factor optimizes two consecutive poses of the body assuming a rolling shutter model of the camera with given readout time.
* The factor requires that values contain (for each pixel observation) two consecutive camera poses
* from which the pixel observation pose can be interpolated.
* @addtogroup SLAM
*/
template<class CAMERA>
class SmartProjectionPoseFactorRollingShutter : public SmartProjectionFactor<CAMERA> {
public:
typedef typename CAMERA::CalibrationType CALIBRATION;
protected:
/// shared pointer to calibration object (one for each observation)
std::vector<boost::shared_ptr<CALIBRATION> > K_all_;
/// The keys of the pose of the body (with respect to an external world frame): two consecutive poses for each observation
std::vector<std::pair<Key, Key>> world_P_body_key_pairs_;
/// interpolation factor (one for each observation) to interpolate between pair of consecutive poses
std::vector<double> alphas_;
/// Pose of the camera in the body frame
std::vector<Pose3> body_P_sensors_;
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
/// shorthand for base class type
typedef SmartProjectionFactor<PinholePose<CALIBRATION> > Base;
/// shorthand for this class
typedef SmartProjectionPoseFactorRollingShutter This;
/// shorthand for a smart pointer to a factor
typedef boost::shared_ptr<This> shared_ptr;
static const int DimBlock = 12; ///< size of the variable stacking 2 poses from which the observation pose is interpolated
static const int DimPose = 6; ///< Pose3 dimension
static const int ZDim = 2; ///< Measurement dimension (Point2)
typedef Eigen::Matrix<double, ZDim, DimBlock> MatrixZD; // F blocks (derivatives wrt block of 2 poses)
typedef std::vector<MatrixZD, Eigen::aligned_allocator<MatrixZD> > FBlocks; // vector of F blocks
/**
* Constructor
* @param Isotropic measurement noise
* @param params internal parameters of the smart factors
*/
SmartProjectionPoseFactorRollingShutter(
const SharedNoiseModel& sharedNoiseModel,
const SmartProjectionParams& params = SmartProjectionParams())
: Base(sharedNoiseModel, params) {
}
/** Virtual destructor */
~SmartProjectionPoseFactorRollingShutter() override = default;
/**
* add a new measurement, with 2 pose keys, interpolation factor, camera (intrinsic and extrinsic) calibration, and observed pixel.
* @param measured 2-dimensional location of the projection of a
* single landmark in a single view (the measurement), interpolated from the 2 poses
* @param world_P_body_key1 key corresponding to the first body poses (time <= time pixel is acquired)
* @param world_P_body_key2 key corresponding to the second body poses (time >= time pixel is acquired)
* @param alpha interpolation factor in [0,1], such that if alpha = 0 the interpolated pose is the same as world_P_body_key1
* @param K (fixed) camera intrinsic calibration
* @param body_P_sensor (fixed) camera extrinsic calibration
*/
void add(const Point2& measured, const Key& world_P_body_key1,
const Key& world_P_body_key2, const double& alpha,
const boost::shared_ptr<CALIBRATION>& K, const Pose3 body_P_sensor = Pose3::identity()) {
// store measurements in base class
this->measured_.push_back(measured);
// store the pair of keys for each measurement, in the same order
world_P_body_key_pairs_.push_back(
std::make_pair(world_P_body_key1, world_P_body_key2));
// also store keys in the keys_ vector: these keys are assumed to be unique, so we avoid duplicates here
if (std::find(this->keys_.begin(), this->keys_.end(), world_P_body_key1) == this->keys_.end())
this->keys_.push_back(world_P_body_key1); // add only unique keys
if (std::find(this->keys_.begin(), this->keys_.end(), world_P_body_key2) == this->keys_.end())
this->keys_.push_back(world_P_body_key2); // add only unique keys
// store interpolation factor
alphas_.push_back(alpha);
// store fixed intrinsic calibration
K_all_.push_back(K);
// store fixed extrinsics of the camera
body_P_sensors_.push_back(body_P_sensor);
}
/**
* Variant of the previous "add" function in which we include multiple measurements
* @param measurements vector of the 2m dimensional location of the projection
* of a single landmark in the m views (the measurements)
* @param world_P_body_key_pairs vector where the i-th element contains a pair of keys corresponding
* to the pair of poses from which the observation pose for the i0-th measurement can be interpolated
* @param alphas vector of interpolation params (in [0,1]), one for each measurement (in the same order)
* @param Ks vector of (fixed) intrinsic calibration objects
* @param body_P_sensors vector of (fixed) extrinsic calibration objects
*/
void add(const Point2Vector& measurements,
const std::vector<std::pair<Key, Key>>& world_P_body_key_pairs,
const std::vector<double>& alphas,
const std::vector<boost::shared_ptr<CALIBRATION>>& Ks,
const std::vector<Pose3> body_P_sensors) {
assert(world_P_body_key_pairs.size() == measurements.size());
assert(world_P_body_key_pairs.size() == alphas.size());
assert(world_P_body_key_pairs.size() == Ks.size());
for (size_t i = 0; i < measurements.size(); i++) {
add(measurements[i], world_P_body_key_pairs[i].first,
world_P_body_key_pairs[i].second, alphas[i], Ks[i],
body_P_sensors[i]);
}
}
/**
* Variant of the previous "add" function in which we include multiple measurements
* with the same (intrinsic and extrinsic) calibration
* @param measurements vector of the 2m dimensional location of the projection
* of a single landmark in the m views (the measurements)
* @param world_P_body_key_pairs vector where the i-th element contains a pair of keys corresponding
* to the pair of poses from which the observation pose for the i0-th measurement can be interpolated
* @param alphas vector of interpolation params (in [0,1]), one for each measurement (in the same order)
* @param K (fixed) camera intrinsic calibration (same for all measurements)
* @param body_P_sensor (fixed) camera extrinsic calibration (same for all measurements)
*/
void add(const Point2Vector& measurements,
const std::vector<std::pair<Key, Key>>& world_P_body_key_pairs,
const std::vector<double>& alphas,
const boost::shared_ptr<CALIBRATION>& K, const Pose3 body_P_sensor = Pose3::identity()) {
assert(world_P_body_key_pairs.size() == measurements.size());
assert(world_P_body_key_pairs.size() == alphas.size());
for (size_t i = 0; i < measurements.size(); i++) {
add(measurements[i], world_P_body_key_pairs[i].first,
world_P_body_key_pairs[i].second, alphas[i], K, body_P_sensor);
}
}
/// return the calibration object
inline std::vector<boost::shared_ptr<CALIBRATION>> calibration() const {
return K_all_;
}
/// return (for each observation) the keys of the pair of poses from which we interpolate
const std::vector<std::pair<Key, Key>> world_P_body_key_pairs() const {
return world_P_body_key_pairs_;
}
/// return the interpolation factors alphas
const std::vector<double> alphas() const {
return alphas_;
}
/// return the extrinsic camera calibration body_P_sensors
const std::vector<Pose3> body_P_sensors() const {
return body_P_sensors_;
}
/**
* 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 override {
std::cout << s << "SmartProjectionPoseFactorRollingShutter: \n ";
for (size_t i = 0; i < K_all_.size(); i++) {
std::cout << "-- Measurement nr " << i << std::endl;
std::cout << " pose1 key: "
<< keyFormatter(world_P_body_key_pairs_[i].first) << std::endl;
std::cout << " pose2 key: "
<< keyFormatter(world_P_body_key_pairs_[i].second) << std::endl;
std::cout << " alpha: " << alphas_[i] << std::endl;
body_P_sensors_[i].print("extrinsic calibration:\n");
K_all_[i]->print("intrinsic calibration = ");
}
Base::print("", keyFormatter);
}
/// equals
bool equals(const NonlinearFactor& p, double tol = 1e-9) const override {
const SmartProjectionPoseFactorRollingShutter<CAMERA>* e =
dynamic_cast<const SmartProjectionPoseFactorRollingShutter<CAMERA>*>(&p);
double keyPairsEqual = true;
if(this->world_P_body_key_pairs_.size() == e->world_P_body_key_pairs().size()){
for(size_t k=0; k< this->world_P_body_key_pairs_.size(); k++){
const Key key1own = world_P_body_key_pairs_[k].first;
const Key key1e = e->world_P_body_key_pairs()[k].first;
const Key key2own = world_P_body_key_pairs_[k].second;
const Key key2e = e->world_P_body_key_pairs()[k].second;
if ( !(key1own == key1e) || !(key2own == key2e) ){
keyPairsEqual = false; break;
}
}
} else {
keyPairsEqual = false;
}
double extrinsicCalibrationEqual = true;
if(this->body_P_sensors_.size() == e->body_P_sensors().size()){
for(size_t i=0; i< this->body_P_sensors_.size(); i++){
if (!body_P_sensors_[i].equals(e->body_P_sensors()[i])){
extrinsicCalibrationEqual = false; break;
}
}
} else {
extrinsicCalibrationEqual = false;
}
return e && Base::equals(p, tol) && K_all_ == e->calibration()
&& alphas_ == e->alphas() && keyPairsEqual && extrinsicCalibrationEqual;
}
/**
* Compute jacobian F, E and error vector at a given linearization point
* @param values Values structure which must contain camera poses
* corresponding to keys involved in this factor
* @return Return arguments are the camera jacobians Fs (including the
* jacobian with respect to both body poses we interpolate from), the point
* Jacobian E, and the error vector b. Note that the jacobians are computed
* for a given point.
*/
void computeJacobiansWithTriangulatedPoint(FBlocks& Fs, Matrix& E, Vector& b,
const Values& values) const {
if (!this->result_) {
throw("computeJacobiansWithTriangulatedPoint");
} else { // valid result: compute jacobians
size_t numViews = this->measured_.size();
E = Matrix::Zero(2 * numViews, 3); // a Point2 for each view (point jacobian)
b = Vector::Zero(2 * numViews); // a Point2 for each view
// intermediate Jacobians
Eigen::Matrix<double, ZDim, DimPose> dProject_dPoseCam;
Eigen::Matrix<double, DimPose, DimPose> dInterpPose_dPoseBody1,
dInterpPose_dPoseBody2, dPoseCam_dInterpPose;
Eigen::Matrix<double, ZDim, 3> Ei;
for (size_t i = 0; i < numViews; i++) { // for each camera/measurement
auto w_P_body1 = values.at<Pose3>(world_P_body_key_pairs_[i].first);
auto w_P_body2 = values.at<Pose3>(world_P_body_key_pairs_[i].second);
double interpolationFactor = alphas_[i];
// get interpolated pose:
auto w_P_body =
interpolate<Pose3>(w_P_body1, w_P_body2, interpolationFactor,
dInterpPose_dPoseBody1, dInterpPose_dPoseBody2);
auto body_P_cam = body_P_sensors_[i];
auto w_P_cam = w_P_body.compose(body_P_cam, dPoseCam_dInterpPose);
PinholeCamera<CALIBRATION> camera(w_P_cam, *K_all_[i]);
// get jacobians and error vector for current measurement
Point2 reprojectionError_i = Point2(
camera.project(*this->result_, dProject_dPoseCam, Ei)
- this->measured_.at(i));
Eigen::Matrix<double, ZDim, DimBlock> J; // 2 x 12
J.block(0, 0, ZDim, 6) = dProject_dPoseCam * dPoseCam_dInterpPose
* dInterpPose_dPoseBody1; // (2x6) * (6x6) * (6x6)
J.block(0, 6, ZDim, 6) = dProject_dPoseCam * dPoseCam_dInterpPose
* dInterpPose_dPoseBody2; // (2x6) * (6x6) * (6x6)
// fit into the output structures
Fs.push_back(J);
size_t row = 2 * i;
b.segment<ZDim>(row) = -reprojectionError_i;
E.block<ZDim, 3>(row, 0) = Ei;
}
}
}
/// linearize and return a Hessianfactor that is an approximation of error(p)
boost::shared_ptr<RegularHessianFactor<DimPose>> createHessianFactor(
const Values& values, const double lambda = 0.0,
bool diagonalDamping = false) const {
// we may have multiple observation sharing the same keys (due to the
// rolling shutter interpolation), hence the number of unique keys may be
// smaller than 2 * nrMeasurements
size_t nrUniqueKeys =
this->keys_
.size(); // note: by construction, keys_ only contains unique keys
// Create structures for Hessian Factors
KeyVector js;
std::vector<Matrix> Gs(nrUniqueKeys * (nrUniqueKeys + 1) / 2);
std::vector<Vector> gs(nrUniqueKeys);
if (this->measured_.size() !=
this->cameras(values).size()) // 1 observation per interpolated camera
throw std::runtime_error(
"SmartProjectionPoseFactorRollingShutter: "
"measured_.size() inconsistent with input");
// triangulate 3D point at given linearization point
this->triangulateSafe(this->cameras(values));
if (!this->result_) { // failed: return "empty/zero" Hessian
if (this->params_.degeneracyMode == ZERO_ON_DEGENERACY) {
for (Matrix& m : Gs) m = Matrix::Zero(DimPose, DimPose);
for (Vector& v : gs) v = Vector::Zero(DimPose);
return boost::make_shared<RegularHessianFactor<DimPose>>(this->keys_,
Gs, gs, 0.0);
} else {
throw std::runtime_error(
"SmartProjectionPoseFactorRollingShutter: "
"only supported degeneracy mode is ZERO_ON_DEGENERACY");
}
}
// compute Jacobian given triangulated 3D Point
FBlocks Fs;
Matrix E;
Vector b;
this->computeJacobiansWithTriangulatedPoint(Fs, E, b, values);
// Whiten using noise model
this->noiseModel_->WhitenSystem(E, b);
for (size_t i = 0; i < Fs.size(); i++)
Fs[i] = this->noiseModel_->Whiten(Fs[i]);
Matrix3 P = Base::Cameras::PointCov(E, lambda, diagonalDamping);
// Collect all the key pairs: these are the keys that correspond to the blocks in Fs (on which we apply the Schur Complement)
KeyVector nonuniqueKeys;
for (size_t i = 0; i < world_P_body_key_pairs_.size(); i++) {
nonuniqueKeys.push_back(world_P_body_key_pairs_.at(i).first);
nonuniqueKeys.push_back(world_P_body_key_pairs_.at(i).second);
}
// Build augmented Hessian (with last row/column being the information vector)
// Note: we need to get the augumented hessian wrt the unique keys in key_
SymmetricBlockMatrix augmentedHessianUniqueKeys =
Base::Cameras::template SchurComplementAndRearrangeBlocks<3, 12, 6>(
Fs, E, P, b, nonuniqueKeys, this->keys_);
return boost::make_shared < RegularHessianFactor<DimPose>
> (this->keys_, augmentedHessianUniqueKeys);
}
/**
* error calculates the error of the factor.
*/
double error(const Values& values) const override {
if (this->active(values)) {
return this->totalReprojectionError(this->cameras(values));
} else { // else of active flag
return 0.0;
}
}
/**
* Collect all cameras involved in this factor
* @param values Values structure which must contain camera poses
* corresponding to keys involved in this factor
* @return Cameras
*/
typename Base::Cameras cameras(const Values& values) const override {
size_t numViews = this->measured_.size();
assert(numViews == K_all_.size());
assert(numViews == alphas_.size());
assert(numViews == body_P_sensors_.size());
assert(numViews == world_P_body_key_pairs_.size());
typename Base::Cameras cameras;
for (size_t i = 0; i < numViews; i++) { // for each measurement
const Pose3& w_P_body1 = values.at<Pose3>(world_P_body_key_pairs_[i].first);
const Pose3& w_P_body2 = values.at<Pose3>(world_P_body_key_pairs_[i].second);
double interpolationFactor = alphas_[i];
const Pose3& w_P_body = interpolate<Pose3>(w_P_body1, w_P_body2, interpolationFactor);
const Pose3& body_P_cam = body_P_sensors_[i];
const Pose3& w_P_cam = w_P_body.compose(body_P_cam);
cameras.emplace_back(w_P_cam, K_all_[i]);
}
return cameras;
}
/**
* Linearize to Gaussian Factor (possibly adding a damping factor Lambda for LM)
* @param values Values structure which must contain camera poses and extrinsic pose for this factor
* @return a Gaussian factor
*/
boost::shared_ptr<GaussianFactor> linearizeDamped(
const Values& values, const double lambda = 0.0) const {
// depending on flag set on construction we may linearize to different linear factors
switch (this->params_.linearizationMode) {
case HESSIAN:
return this->createHessianFactor(values, lambda);
default:
throw std::runtime_error(
"SmartProjectionPoseFactorRollingShutter: unknown linearization mode");
}
}
/// linearize
boost::shared_ptr<GaussianFactor> linearize(const Values& values) const
override {
return this->linearizeDamped(values);
}
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(K_all_);
}
};
// end of class declaration
/// traits
template<class CAMERA>
struct traits<SmartProjectionPoseFactorRollingShutter<CAMERA> > :
public Testable<SmartProjectionPoseFactorRollingShutter<CAMERA> > {
};
} // namespace gtsam
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