CameraSet.h

Go to the documentation of this file.

/* ----------------------------------------------------------------------------
 
 * 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
 
 * -------------------------------------------------------------------------- */
 
#pragma once
 
#include <gtsam/geometry/Point3.h>
#include <gtsam/geometry/CalibratedCamera.h>  // for Cheirality exception
#include <gtsam/base/Testable.h>
#include <gtsam/base/SymmetricBlockMatrix.h>
#include <gtsam/base/FastMap.h>
#include <gtsam/inference/Key.h>
#include <vector>
 
namespace gtsam {
 
template<class CAMERA>
class CameraSet : public std::vector<CAMERA, Eigen::aligned_allocator<CAMERA> > {
 
protected:
 
  typedef typename CAMERA::Measurement Z;
  typedef typename CAMERA::MeasurementVector ZVector;
 
  static const int D = traits<CAMERA>::dimension; 
  static const int ZDim = traits<Z>::dimension; 
 
  static Vector ErrorVector(const ZVector& predicted,
      const ZVector& measured) {
 
    // Check size
    size_t m = predicted.size();
    if (measured.size() != m)
      throw std::runtime_error("CameraSet::errors: size mismatch");
 
    // Project and fill error vector
    Vector b(ZDim * m);
    for (size_t i = 0, row = 0; i < m; i++, row += ZDim) {
      Vector bi = traits<Z>::Local(measured[i], predicted[i]);
      if(ZDim==3 && std::isnan(bi(1))){ // if it is a stereo point and the right pixel is missing (nan)
        bi(1) = 0;
      }
      b.segment<ZDim>(row) = bi;
    }
    return b;
  }
 
public:
 
  virtual ~CameraSet() = default;
 
  using MatrixZD = Eigen::Matrix<double, ZDim, D>;
  using FBlocks = std::vector<MatrixZD, Eigen::aligned_allocator<MatrixZD>>;
 
  virtual void print(const std::string& s = "") const {
    std::cout << s << "CameraSet, cameras = \n";
    for (size_t k = 0; k < this->size(); ++k)
      this->at(k).print(s);
  }
 
  bool equals(const CameraSet& p, double tol = 1e-9) const {
    if (this->size() != p.size())
      return false;
    bool camerasAreEqual = true;
    for (size_t i = 0; i < this->size(); i++) {
      if (this->at(i).equals(p.at(i), tol) == false)
        camerasAreEqual = false;
      break;
    }
    return camerasAreEqual;
  }
 
  template<class POINT>
  ZVector project2(const POINT& point, //
      boost::optional<FBlocks&> Fs = boost::none, //
      boost::optional<Matrix&> E = boost::none) const {
 
    static const int N = FixedDimension<POINT>::value;
 
    // Allocate result
    size_t m = this->size();
    ZVector z;
    z.reserve(m);
 
    // Allocate derivatives
    if (E) E->resize(ZDim * m, N);
    if (Fs) Fs->resize(m);
 
    // Project and fill derivatives
    for (size_t i = 0; i < m; i++) {
      MatrixZD Fi;
      Eigen::Matrix<double, ZDim, N> Ei;
      z.emplace_back(this->at(i).project2(point, Fs ? &Fi : 0, E ? &Ei : 0));
      if (Fs) (*Fs)[i] = Fi;
      if (E) E->block<ZDim, N>(ZDim * i, 0) = Ei;
    }
 
    return z;
  }
 
  template<class POINT>
  Vector reprojectionError(const POINT& point, const ZVector& measured,
      boost::optional<FBlocks&> Fs = boost::none, //
      boost::optional<Matrix&> E = boost::none) const {
    return ErrorVector(project2(point, Fs, E), measured);
  }
 
  template <int N,
            int ND>  // N = 2 or 3 (point dimension), ND is the camera dimension
  static SymmetricBlockMatrix SchurComplement(
      const std::vector<
          Eigen::Matrix<double, ZDim, ND>,
          Eigen::aligned_allocator<Eigen::Matrix<double, ZDim, ND>>>& Fs,
      const Matrix& E, const Eigen::Matrix<double, N, N>& P, const Vector& b) {
    // a single point is observed in m cameras
    size_t m = Fs.size();
 
    // Create a SymmetricBlockMatrix (augmented hessian, with extra row/column with info vector)
    size_t M1 = ND * m + 1;
    std::vector<DenseIndex> dims(m + 1); // this also includes the b term
    std::fill(dims.begin(), dims.end() - 1, ND);
    dims.back() = 1;
    SymmetricBlockMatrix augmentedHessian(dims, Matrix::Zero(M1, M1));
 
    // Blockwise Schur complement
    for (size_t i = 0; i < m; i++) { // for each camera
 
      const Eigen::Matrix<double, ZDim, ND>& Fi = Fs[i];
      const auto FiT = Fi.transpose();
      const Eigen::Matrix<double, ZDim, N> Ei_P = //
          E.block(ZDim * i, 0, ZDim, N) * P;
 
      // D = (Dx2) * ZDim
      augmentedHessian.setOffDiagonalBlock(i, m, FiT * b.segment<ZDim>(ZDim * i) // F' * b
      - FiT * (Ei_P * (E.transpose() * b))); // D = (DxZDim) * (ZDimx3) * (N*ZDimm) * (ZDimm x 1)
 
      // (DxD) = (DxZDim) * ( (ZDimxD) - (ZDimx3) * (3xZDim) * (ZDimxD) )
      augmentedHessian.setDiagonalBlock(i, FiT
          * (Fi - Ei_P * E.block(ZDim * i, 0, ZDim, N).transpose() * Fi));
 
      // upper triangular part of the hessian
      for (size_t j = i + 1; j < m; j++) { // for each camera
        const Eigen::Matrix<double, ZDim, ND>& Fj = Fs[j];
 
        // (DxD) = (Dx2) * ( (2x2) * (2xD) )
        augmentedHessian.setOffDiagonalBlock(i, j, -FiT
            * (Ei_P * E.block(ZDim * j, 0, ZDim, N).transpose() * Fj));
      }
    } // end of for over cameras
 
    augmentedHessian.diagonalBlock(m)(0, 0) += b.squaredNorm();
    return augmentedHessian;
  }
 
  template <int N, int ND, int NDD>
  static SymmetricBlockMatrix SchurComplementAndRearrangeBlocks(
      const std::vector<
          Eigen::Matrix<double, ZDim, ND>,
          Eigen::aligned_allocator<Eigen::Matrix<double, ZDim, ND>>>& Fs,
      const Matrix& E, const Eigen::Matrix<double, N, N>& P, const Vector& b,
      const KeyVector& jacobianKeys, const KeyVector& hessianKeys) {
    size_t nrNonuniqueKeys = jacobianKeys.size();
    size_t nrUniqueKeys = hessianKeys.size();
 
    // Marginalize point: note - we reuse the standard SchurComplement function.
    SymmetricBlockMatrix augmentedHessian = SchurComplement<N, ND>(Fs, E, P, b);
 
    // Pack into an Hessian factor, allow space for b term.
    std::vector<DenseIndex> dims(nrUniqueKeys + 1);
    std::fill(dims.begin(), dims.end() - 1, NDD);
    dims.back() = 1;
    SymmetricBlockMatrix augmentedHessianUniqueKeys;
 
    // Deal with the fact that some blocks may share the same keys.
    if (nrUniqueKeys == nrNonuniqueKeys) {
      // Case when there is 1 calibration key per camera:
      augmentedHessianUniqueKeys = SymmetricBlockMatrix(
          dims, Matrix(augmentedHessian.selfadjointView()));
    } else {
      // When multiple cameras share a calibration we have to rearrange
      // the results of the Schur complement matrix.
      std::vector<DenseIndex> nonuniqueDims(nrNonuniqueKeys + 1);  // includes b
      std::fill(nonuniqueDims.begin(), nonuniqueDims.end() - 1, NDD);
      nonuniqueDims.back() = 1;
      augmentedHessian = SymmetricBlockMatrix(
          nonuniqueDims, Matrix(augmentedHessian.selfadjointView()));
 
      // Get map from key to location in the new augmented Hessian matrix (the
      // one including only unique keys).
      std::map<Key, size_t> keyToSlotMap;
      for (size_t k = 0; k < nrUniqueKeys; k++) {
        keyToSlotMap[hessianKeys[k]] = k;
      }
 
      // Initialize matrix to zero.
      augmentedHessianUniqueKeys = SymmetricBlockMatrix(
          dims, Matrix::Zero(NDD * nrUniqueKeys + 1, NDD * nrUniqueKeys + 1));
 
      // Add contributions for each key: note this loops over the hessian with
      // nonUnique keys (augmentedHessian) and populates an Hessian that only
      // includes the unique keys (that is what we want to return).
      for (size_t i = 0; i < nrNonuniqueKeys; i++) {  // rows
        Key key_i = jacobianKeys.at(i);
 
        // Update information vector.
        augmentedHessianUniqueKeys.updateOffDiagonalBlock(
            keyToSlotMap[key_i], nrUniqueKeys,
            augmentedHessian.aboveDiagonalBlock(i, nrNonuniqueKeys));
 
        // Update blocks.
        for (size_t j = i; j < nrNonuniqueKeys; j++) {  // cols
          Key key_j = jacobianKeys.at(j);
          if (i == j) {
            augmentedHessianUniqueKeys.updateDiagonalBlock(
                keyToSlotMap[key_i], augmentedHessian.diagonalBlock(i));
          } else {  // (i < j)
            if (keyToSlotMap[key_i] != keyToSlotMap[key_j]) {
              augmentedHessianUniqueKeys.updateOffDiagonalBlock(
                  keyToSlotMap[key_i], keyToSlotMap[key_j],
                  augmentedHessian.aboveDiagonalBlock(i, j));
            } else {
              augmentedHessianUniqueKeys.updateDiagonalBlock(
                  keyToSlotMap[key_i],
                  augmentedHessian.aboveDiagonalBlock(i, j) +
                      augmentedHessian.aboveDiagonalBlock(i, j).transpose());
            }
          }
        }
      }
 
      // Update bottom right element of the matrix.
      augmentedHessianUniqueKeys.updateDiagonalBlock(
          nrUniqueKeys, augmentedHessian.diagonalBlock(nrNonuniqueKeys));
    }
    return augmentedHessianUniqueKeys;
  }
 
  template<int N> // N = 2 or 3
  static SymmetricBlockMatrix SchurComplement(const FBlocks& Fs,
      const Matrix& E, const Eigen::Matrix<double, N, N>& P, const Vector& b) {
    return SchurComplement<N,D>(Fs, E, P, b);
  }
 
  template<int N> // N = 2 or 3 (point dimension)
  static void ComputePointCovariance(Eigen::Matrix<double, N, N>& P,
      const Matrix& E, double lambda, bool diagonalDamping = false) {
 
    Matrix EtE = E.transpose() * E;
 
    if (diagonalDamping) { // diagonal of the hessian
      EtE.diagonal() += lambda * EtE.diagonal();
    } else {
      DenseIndex n = E.cols();
      EtE += lambda * Eigen::MatrixXd::Identity(n, n);
    }
 
    P = (EtE).inverse();
  }
 
  static Matrix PointCov(const Matrix& E, const double lambda = 0.0,
      bool diagonalDamping = false) {
    if (E.cols() == 2) {
      Matrix2 P2;
      ComputePointCovariance<2>(P2, E, lambda, diagonalDamping);
      return P2;
    } else {
      Matrix3 P3;
      ComputePointCovariance<3>(P3, E, lambda, diagonalDamping);
      return P3;
    }
  }
 
  static SymmetricBlockMatrix SchurComplement(const FBlocks& Fblocks,
      const Matrix& E, const Vector& b, const double lambda = 0.0,
      bool diagonalDamping = false) {
    if (E.cols() == 2) {
      Matrix2 P;
      ComputePointCovariance<2>(P, E, lambda, diagonalDamping);
      return SchurComplement<2>(Fblocks, E, P, b);
    } else {
      Matrix3 P;
      ComputePointCovariance<3>(P, E, lambda, diagonalDamping);
      return SchurComplement<3>(Fblocks, E, P, b);
    }
  }
 
  template<int N> // N = 2 or 3 (point dimension)
  static void UpdateSchurComplement(const FBlocks& Fs, const Matrix& E,
      const Eigen::Matrix<double, N, N>& P, const Vector& b,
      const KeyVector& allKeys, const KeyVector& keys,
      /*output ->*/SymmetricBlockMatrix& augmentedHessian) {
 
    assert(keys.size()==Fs.size());
    assert(keys.size()<=allKeys.size());
 
    FastMap<Key, size_t> KeySlotMap;
    for (size_t slot = 0; slot < allKeys.size(); slot++)
      KeySlotMap.insert(std::make_pair(allKeys[slot], slot));
 
    // Schur complement trick
    // G = F' * F - F' * E * P * E' * F
    // g = F' * (b - E * P * E' * b)
 
    // a single point is observed in m cameras
    size_t m = Fs.size(); // cameras observing current point
    size_t M = (augmentedHessian.rows() - 1) / D; // all cameras in the group
    assert(allKeys.size()==M);
 
    // Blockwise Schur complement
    for (size_t i = 0; i < m; i++) { // for each camera in the current factor
 
      const MatrixZD& Fi = Fs[i];
      const auto FiT = Fi.transpose();
      const Eigen::Matrix<double, 2, N> Ei_P = E.template block<ZDim, N>(
          ZDim * i, 0) * P;
 
      // D = (DxZDim) * (ZDim)
      // allKeys are the list of all camera keys in the group, e.g, (1,3,4,5,7)
      // we should map those to a slot in the local (grouped) hessian (0,1,2,3,4)
      // Key cameraKey_i = this->keys_[i];
      DenseIndex aug_i = KeySlotMap.at(keys[i]);
 
      // information vector - store previous vector
      // vectorBlock = augmentedHessian(aug_i, aug_m).knownOffDiagonal();
      // add contribution of current factor
      augmentedHessian.updateOffDiagonalBlock(aug_i, M,
          FiT * b.segment<ZDim>(ZDim * i)      // F' * b
        - FiT * (Ei_P * (E.transpose() * b))); // D = (DxZDim) * (ZDimx3) * (N*ZDimm) * (ZDimm x 1)
 
      // (DxD) += (DxZDim) * ( (ZDimxD) - (ZDimx3) * (3xZDim) * (ZDimxD) )
      // add contribution of current factor
      // TODO(gareth): Eigen doesn't let us pass the expression. Call eval() for now...
      augmentedHessian.updateDiagonalBlock(aug_i,
         ((FiT * (Fi - Ei_P * E.template block<ZDim, N>(ZDim * i, 0).transpose() * Fi))).eval());
 
      // upper triangular part of the hessian
      for (size_t j = i + 1; j < m; j++) { // for each camera
        const MatrixZD& Fj = Fs[j];
 
        DenseIndex aug_j = KeySlotMap.at(keys[j]);
 
        // (DxD) = (DxZDim) * ( (ZDimxZDim) * (ZDimxD) )
        // off diagonal block - store previous block
        // matrixBlock = augmentedHessian(aug_i, aug_j).knownOffDiagonal();
        // add contribution of current factor
        augmentedHessian.updateOffDiagonalBlock(aug_i, aug_j,
                -FiT * (Ei_P * E.template block<ZDim, N>(ZDim * j, 0).transpose() * Fj));
      }
    } // end of for over cameras
 
    augmentedHessian.diagonalBlock(M)(0, 0) += b.squaredNorm();
  }
 
private:
 
  friend class boost::serialization::access;
  template<class ARCHIVE>
  void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
    ar & (*this);
  }
 
public:
  GTSAM_MAKE_ALIGNED_OPERATOR_NEW
};
 
template<class CAMERA>
const int CameraSet<CAMERA>::D;
 
template<class CAMERA>
const int CameraSet<CAMERA>::ZDim;
 
template<class CAMERA>
struct traits<CameraSet<CAMERA> > : public Testable<CameraSet<CAMERA> > {
};
 
template<class CAMERA>
struct traits<const CameraSet<CAMERA> > : public Testable<CameraSet<CAMERA> > {
};
 
} // \ namespace gtsam