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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2016 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: vitus@google.com (Michael Vitus)
#ifndef EXAMPLES_CERES_POSE_GRAPH_3D_ERROR_TERM_H_
#define EXAMPLES_CERES_POSE_GRAPH_3D_ERROR_TERM_H_
#include "Eigen/Core"
#include "ceres/autodiff_cost_function.h"
#include "types.h"
namespace ceres {
namespace examples {
// Computes the error term for two poses that have a relative pose measurement
// between them. Let the hat variables be the measurement. We have two poses x_a
// and x_b. Through sensor measurements we can measure the transformation of
// frame B w.r.t frame A denoted as t_ab_hat. We can compute an error metric
// between the current estimate of the poses and the measurement.
//
// In this formulation, we have chosen to represent the rigid transformation as
// a Hamiltonian quaternion, q, and position, p. The quaternion ordering is
// [x, y, z, w].
// The estimated measurement is:
// t_ab = [ p_ab ] = [ R(q_a)^T * (p_b - p_a) ]
// [ q_ab ] [ q_a^{-1] * q_b ]
//
// where ^{-1} denotes the inverse and R(q) is the rotation matrix for the
// quaternion. Now we can compute an error metric between the estimated and
// measurement transformation. For the orientation error, we will use the
// standard multiplicative error resulting in:
//
// error = [ p_ab - \hat{p}_ab ]
// [ 2.0 * Vec(q_ab * \hat{q}_ab^{-1}) ]
//
// where Vec(*) returns the vector (imaginary) part of the quaternion. Since
// the measurement has an uncertainty associated with how accurate it is, we
// will weight the errors by the square root of the measurement information
// matrix:
//
// residuals = I^{1/2) * error
// where I is the information matrix which is the inverse of the covariance.
class PoseGraph3dErrorTerm {
public:
PoseGraph3dErrorTerm(const Pose3d& t_ab_measured,
const Eigen::Matrix<double, 6, 6>& sqrt_information)
: t_ab_measured_(t_ab_measured), sqrt_information_(sqrt_information) {}
template <typename T>
bool operator()(const T* const p_a_ptr, const T* const q_a_ptr,
const T* const p_b_ptr, const T* const q_b_ptr,
T* residuals_ptr) const {
Eigen::Map<const Eigen::Matrix<T, 3, 1> > p_a(p_a_ptr);
Eigen::Map<const Eigen::Quaternion<T> > q_a(q_a_ptr);
Eigen::Map<const Eigen::Matrix<T, 3, 1> > p_b(p_b_ptr);
Eigen::Map<const Eigen::Quaternion<T> > q_b(q_b_ptr);
// Compute the relative transformation between the two frames.
Eigen::Quaternion<T> q_a_inverse = q_a.conjugate();
Eigen::Quaternion<T> q_ab_estimated = q_a_inverse * q_b;
// Represent the displacement between the two frames in the A frame.
Eigen::Matrix<T, 3, 1> p_ab_estimated = q_a_inverse * (p_b - p_a);
// Compute the error between the two orientation estimates.
Eigen::Quaternion<T> delta_q =
t_ab_measured_.q.template cast<T>() * q_ab_estimated.conjugate();
// Compute the residuals.
// [ position ] [ delta_p ]
// [ orientation (3x1)] = [ 2 * delta_q(0:2) ]
Eigen::Map<Eigen::Matrix<T, 6, 1> > residuals(residuals_ptr);
residuals.template block<3, 1>(0, 0) =
p_ab_estimated - t_ab_measured_.p.template cast<T>();
residuals.template block<3, 1>(3, 0) = T(2.0) * delta_q.vec();
// Scale the residuals by the measurement uncertainty.
residuals.applyOnTheLeft(sqrt_information_.template cast<T>());
return true;
}
static ceres::CostFunction* Create(
const Pose3d& t_ab_measured,
const Eigen::Matrix<double, 6, 6>& sqrt_information) {
return new ceres::AutoDiffCostFunction<PoseGraph3dErrorTerm, 6, 3, 4, 3, 4>(
new PoseGraph3dErrorTerm(t_ab_measured, sqrt_information));
}
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
// The measurement for the position of B relative to A in the A frame.
const Pose3d t_ab_measured_;
// The square root of the measurement information matrix.
const Eigen::Matrix<double, 6, 6> sqrt_information_;
};
} // namespace examples
} // namespace ceres
#endif // EXAMPLES_CERES_POSE_GRAPH_3D_ERROR_TERM_H_