fixing tests by moving to Cal3_S2
Ayush Baid
parent
8ca7f1ff1d
commit
f60e9e9365
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@ -12,7 +12,8 @@
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/**
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* @file EssentialMatrixWithCalibrationFactor.h
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*
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* @brief A factor evaluating algebraic epipolar error with essential matrix and calibration as variables.
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* @brief A factor evaluating algebraic epipolar error with essential matrix and
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* calibration as variables.
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*
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* @author Ayush Baid
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* @author Akshay Krishnan
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@ -21,38 +22,40 @@
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#pragma once
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#include <gtsam/nonlinear/NonlinearFactor.h>
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#include <gtsam/geometry/EssentialMatrix.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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#include <iostream>
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namespace gtsam {
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/**
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* Factor that evaluates algebraic epipolar error (K^-1 p)'E (K^-1 p) for given essential matrix and calibration shared
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* between two images.
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* Factor that evaluates algebraic epipolar error (K^-1 p)'E (K^-1 p) for given
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* essential matrix and calibration shared between two images.
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*/
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template<class CALIBRATION>
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class EssentialMatrixWithCalibrationFactor: public NoiseModelFactor2<EssentialMatrix, CALIBRATION > {
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Point2 pA_, pB_; ///< points in pixel coordinates
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template <class CALIBRATION>
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class EssentialMatrixWithCalibrationFactor
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: public NoiseModelFactor2<EssentialMatrix, CALIBRATION> {
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Point2 pA_, pB_; ///< points in pixel coordinates
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typedef NoiseModelFactor2<EssentialMatrix, CALIBRATION> Base;
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typedef EssentialMatrixWithCalibrationFactor This;
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public:
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public:
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/**
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* Constructor
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* @param essentialMatrixKey Essential Matrix variable key
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* @param calibrationKey Calibration variable key
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* @param pA point in first camera, in pixel coordinates
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* @param pB point in second camera, in pixel coordinates
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* @param model noise model is about dot product in ideal, homogeneous coordinates
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* @param model noise model is about dot product in ideal, homogeneous
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* coordinates
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*/
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EssentialMatrixWithCalibrationFactor(Key essentialMatrixKey, Key calibrationKey, const Point2& pA, const Point2& pB,
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const SharedNoiseModel& model) :
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Base(model, essentialMatrixKey, calibrationKey), pA_(pA), pB_(pB) {}
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EssentialMatrixWithCalibrationFactor(Key essentialMatrixKey,
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Key calibrationKey, const Point2& pA,
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const Point2& pB,
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const SharedNoiseModel& model)
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: Base(model, essentialMatrixKey, calibrationKey), pA_(pA), pB_(pB) {}
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/// @return a deep copy of this factor
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gtsam::NonlinearFactor::shared_ptr clone() const override {
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@ -61,12 +64,13 @@ public:
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}
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/// print
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void print(const std::string& s = "",
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void print(
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const std::string& s = "",
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const KeyFormatter& keyFormatter = DefaultKeyFormatter) const override {
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Base::print(s);
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std::cout << " EssentialMatrixWithCalibrationFactor with measurements\n ("
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<< pA_.transpose() << ")' and (" << pB_.transpose() << ")'"
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<< std::endl;
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<< pA_.transpose() << ")' and (" << pB_.transpose() << ")'"
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<< std::endl;
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}
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/// vector of errors returns 1D vector
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@ -79,12 +83,14 @@ public:
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* @param H2 optional jacobian in K
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* @return * Vector
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*/
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Vector evaluateError(const EssentialMatrix& E, const CALIBRATION& K,
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boost::optional<Matrix&> H1 = boost::none, boost::optional<Matrix&> H2 = boost::none) const override {
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Vector evaluateError(
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const EssentialMatrix& E, const CALIBRATION& K,
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boost::optional<Matrix&> H1 = boost::none,
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boost::optional<Matrix&> H2 = boost::none) const override {
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Vector error(1);
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// converting from pixel coordinates to normalized coordinates cA and cB
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Matrix cA_H_K; // dcA/dK
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Matrix cB_H_K; // dcB/dK
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Matrix cA_H_K; // dcA/dK
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Matrix cB_H_K; // dcB/dK
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Point2 cA = K.calibrate(pA_, cA_H_K);
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Point2 cB = K.calibrate(pB_, cB_H_K);
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@ -92,11 +98,12 @@ public:
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Vector3 vA = EssentialMatrix::Homogeneous(cA);
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Vector3 vB = EssentialMatrix::Homogeneous(cB);
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if (H2){
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if (H2) {
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// compute the jacobian of error w.r.t K
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// dvX / dcX [3x2] = [1, 0], [0, 1], [0, 0]
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Matrix v_H_c = (Matrix(3, 2) << 1.0, 0.0, 0.0, 1.0, 0.0, 0.0).finished(); // [3x2]
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Matrix v_H_c =
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(Matrix(3, 2) << 1.0, 0.0, 0.0, 1.0, 0.0, 0.0).finished(); // [3x2]
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// computing dvA/dK = dvA/dcA * dcA/dK and dVB/dK = dvB/dcB * dcB/dK
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Matrix vA_H_K = v_H_c * cA_H_K;
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@ -104,7 +111,8 @@ public:
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// error function f = vB.T * E * vA
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// H2 = df/dK = vB.T * E.T * dvA/dK + vA.T * E * dvB/dK
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*H2 = vB.transpose() * E.matrix().transpose() * vA_H_K + vA.transpose() * E.matrix() * vB_H_K;
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*H2 = vB.transpose() * E.matrix().transpose() * vA_H_K +
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vA.transpose() * E.matrix() * vB_H_K;
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}
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error << E.error(vA, vB, H1);
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@ -112,8 +120,8 @@ public:
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return error;
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}
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public:
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public:
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GTSAM_MAKE_ALIGNED_OPERATOR_NEW
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};
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}// gtsam
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} // namespace gtsam
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@ -6,27 +6,25 @@
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* @date April 22, 2021
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*/
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#include <gtsam/slam/EssentialMatrixWithCalibrationFactor.h>
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#include <gtsam/slam/dataset.h>
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#include <gtsam/nonlinear/expressionTesting.h>
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#include <gtsam/nonlinear/ExpressionFactor.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/geometry/CalibratedCamera.h>
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#include <gtsam/geometry/Cal3Bundler.h>
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#include <gtsam/geometry/Cal3_S2.h>
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/geometry/Cal3Bundler.h>
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#include <gtsam/geometry/Cal3_S2.h>
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#include <gtsam/geometry/CalibratedCamera.h>
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#include <gtsam/nonlinear/ExpressionFactor.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/expressionTesting.h>
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#include <gtsam/slam/EssentialMatrixWithCalibrationFactor.h>
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#include <gtsam/slam/dataset.h>
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using namespace std;
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using namespace gtsam;
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// Noise model for first type of factor is evaluating algebraic error
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noiseModel::Isotropic::shared_ptr model1 = noiseModel::Isotropic::Sigma(1,
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0.01);
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noiseModel::Isotropic::shared_ptr model1 =
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noiseModel::Isotropic::Sigma(1, 0.01);
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// Noise model for second type of factor is evaluating pixel coordinates
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noiseModel::Unit::shared_ptr model2 = noiseModel::Unit::Create(2);
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@ -41,36 +39,33 @@ SfmData data;
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bool readOK = readBAL(filename, data);
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Rot3 c1Rc2 = data.cameras[1].pose().rotation();
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Point3 c1Tc2 = data.cameras[1].pose().translation();
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Cal3Bundler trueK = data.cameras[1].calibration(); // TODO: maybe default value not good; assert with 0th
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// PinholeCamera<Cal3Bundler> camera2(data.cameras[1].pose(), trueK);
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// TODO: maybe default value not good; assert with 0th
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Cal3_S2 trueK = Cal3_S2();
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// PinholeCamera<Cal3_S2> camera2(data.cameras[1].pose(), trueK);
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Rot3 trueRotation(c1Rc2);
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Unit3 trueDirection(c1Tc2);
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EssentialMatrix trueE(trueRotation, trueDirection);
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double baseline = 0.1; // actual baseline of the camera
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double baseline = 0.1; // actual baseline of the camera
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Point2 pA(size_t i) {
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return data.tracks[i].measurements[0].second;
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}
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Point2 pB(size_t i) {
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return data.tracks[i].measurements[1].second;
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}
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Vector vA(size_t i, Cal3Bundler K) {
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Point2 pA(size_t i) { return data.tracks[i].measurements[0].second; }
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Point2 pB(size_t i) { return data.tracks[i].measurements[1].second; }
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Vector vA(size_t i, Cal3_S2 K) {
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return EssentialMatrix::Homogeneous(K.calibrate(pA(i)));
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}
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Vector vB(size_t i, Cal3Bundler K) {
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Vector vB(size_t i, Cal3_S2 K) {
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return EssentialMatrix::Homogeneous(K.calibrate(pB(i)));
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}
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//*************************************************************************
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TEST (EssentialMatrixWithCalibrationFactor, testData) {
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TEST(EssentialMatrixWithCalibrationFactor, testData) {
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CHECK(readOK);
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// Check E matrix
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Matrix expected(3, 3);
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expected << 0, 0, 0, 0, 0, -0.1, 0.1, 0, 0;
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Matrix aEb_matrix = skewSymmetric(c1Tc2.x(), c1Tc2.y(), c1Tc2.z())
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* c1Rc2.matrix();
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Matrix aEb_matrix =
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skewSymmetric(c1Tc2.x(), c1Tc2.y(), c1Tc2.z()) * c1Rc2.matrix();
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EXPECT(assert_equal(expected, aEb_matrix, 1e-8));
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// Check some projections
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@ -83,13 +78,13 @@ TEST (EssentialMatrixWithCalibrationFactor, testData) {
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EXPECT(assert_equal(Vector3(-1, 0.2, 1), vB(4, trueK), 1e-8));
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// check the calibration
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Cal3Bundler expectedK;
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Cal3_S2 expectedK(1, 1, 0, 0, 0);
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EXPECT(assert_equal(expectedK, trueK));
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// Check epipolar constraint
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for (size_t i = 0; i < 5; i++)
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EXPECT_DOUBLES_EQUAL(0, vA(i, trueK).transpose() * aEb_matrix * vB(i, trueK), 1e-8);
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EXPECT_DOUBLES_EQUAL(
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0, vA(i, trueK).transpose() * aEb_matrix * vB(i, trueK), 1e-8);
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// Check epipolar constraint
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for (size_t i = 0; i < 5; i++)
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@ -97,11 +92,12 @@ TEST (EssentialMatrixWithCalibrationFactor, testData) {
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}
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//*************************************************************************
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TEST (EssentialMatrixWithCalibrationFactor, factor) {
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TEST(EssentialMatrixWithCalibrationFactor, factor) {
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Key keyE(1);
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Key keyK(1);
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for (size_t i = 0; i < 5; i++) {
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EssentialMatrixWithCalibrationFactor<Cal3Bundler> factor(keyE, keyK, pA(i), pB(i), model1);
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EssentialMatrixWithCalibrationFactor<Cal3_S2> factor(keyE, keyK, pA(i),
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pB(i), model1);
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// Check evaluation
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Vector expected(1);
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@ -114,16 +110,20 @@ TEST (EssentialMatrixWithCalibrationFactor, factor) {
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// Use numerical derivatives to calculate the expected Jacobian
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Matrix HEexpected;
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Matrix HKexpected;
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typedef Eigen::Matrix<double,1,1> Vector1;
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typedef Eigen::Matrix<double, 1, 1> Vector1;
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// TODO: fix this
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boost::function<Vector(const EssentialMatrix&, const Cal3Bundler&)> f = boost::bind(
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&EssentialMatrixWithCalibrationFactor<Cal3Bundler>::evaluateError, factor, _1, _2, boost::none, boost::none);
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HEexpected = numericalDerivative21<Vector1, EssentialMatrix, Cal3Bundler>(f, trueE, trueK);
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HKexpected = numericalDerivative22<Vector1, EssentialMatrix, Cal3Bundler>(f, trueE, trueK);
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boost::function<Vector(const EssentialMatrix &, const Cal3_S2 &)> f =
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boost::bind(
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&EssentialMatrixWithCalibrationFactor<Cal3_S2>::evaluateError,
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factor, _1, _2, boost::none, boost::none);
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HEexpected = numericalDerivative21<Vector1, EssentialMatrix, Cal3_S2>(
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f, trueE, trueK);
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HKexpected = numericalDerivative22<Vector1, EssentialMatrix, Cal3_S2>(
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f, trueE, trueK);
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// Verify the Jacobian is correct
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EXPECT(assert_equal(HEexpected, HEactual, 1e-8));
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EXPECT(assert_equal(HKexpected, HKactual, 1e-5));
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EXPECT(assert_equal(HKexpected, HKactual, 1e-8));
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}
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}
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@ -132,11 +132,11 @@ TEST (EssentialMatrixWithCalibrationFactor, factor) {
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// Key keyE(1);
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// Key keyK(2);
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// for (size_t i = 0; i < 5; i++) {
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// boost::function<double(const EssentialMatrix&, const Cal3Bundler&,
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// boost::function<double(const EssentialMatrix&, const Cal3_S2&,
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// OptionalJacobian<1, 5>, OptionalJacobian<1, 3>)> f =
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// boost::bind(&EssentialMatrix::error, _1, pA(i), pB(i), _2);
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// Expression<EssentialMatrix> E_(keyE); // leaf expression
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// Expression<Cal3Bundler> K_(keyK); // leaf expression
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// Expression<Cal3_S2> K_(keyK); // leaf expression
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// Expression<double> expr(f, E_, K_); // unary expression
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// // Test the derivatives using Paul's magic
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@ -162,13 +162,16 @@ TEST (EssentialMatrixWithCalibrationFactor, factor) {
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// Key keyE(1);
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// Key keyK(1);
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// for (size_t i = 0; i < 5; i++) {
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// boost::function<double(const EssentialMatrix&, OptionalJacobian<1, 5>)> f =
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// boost::function<double(const EssentialMatrix&, OptionalJacobian<1, 5>)> f
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// =
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// boost::bind(&EssentialMatrix::error, _1, vA(i), vB(i), _2);
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// boost::function<EssentialMatrix(const Rot3&, const Unit3&, OptionalJacobian<5, 3>,
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// boost::function<EssentialMatrix(const Rot3&, const Unit3&,
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// OptionalJacobian<5, 3>,
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// OptionalJacobian<5, 2>)> g;
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// Expression<Rot3> R_(key);
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// Expression<Unit3> d_(trueDirection);
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// Expression<EssentialMatrix> E_(&EssentialMatrix::FromRotationAndDirection, R_, d_);
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// Expression<EssentialMatrix>
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// E_(&EssentialMatrix::FromRotationAndDirection, R_, d_);
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// Expression<double> expr(f, E_);
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// // Test the derivatives using Paul's magic
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@ -193,7 +196,7 @@ TEST(EssentialMatrixWithCalibrationFactor, evaluateErrorJacobians) {
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Key keyE(1);
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Key keyK(2);
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// initialize essential matrix
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Rot3 r = Rot3::Expmap(Vector3(M_PI/6, M_PI / 3, M_PI/9));
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Rot3 r = Rot3::Expmap(Vector3(M_PI / 6, M_PI / 3, M_PI / 9));
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Unit3 t(Point3(2, -1, 0.5));
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EssentialMatrix E = EssentialMatrix::FromRotationAndDirection(r, t);
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Cal3_S2 K(200, 1, 1, 10, 10);
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@ -209,7 +212,7 @@ TEST(EssentialMatrixWithCalibrationFactor, evaluateErrorJacobians) {
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}
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//*************************************************************************
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TEST (EssentialMatrixWithCalibrationFactor, minimization) {
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TEST(EssentialMatrixWithCalibrationFactor, minimization) {
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// Here we want to optimize directly on essential matrix constraints
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// Yi Ma's algorithm (Ma01ijcv) is a bit cumbersome to implement,
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// but GTSAM does the equivalent anyway, provided we give the right
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@ -219,7 +222,8 @@ TEST (EssentialMatrixWithCalibrationFactor, minimization) {
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// Noise sigma is 1cm, assuming metric measurements
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NonlinearFactorGraph graph;
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for (size_t i = 0; i < 5; i++)
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graph.emplace_shared<EssentialMatrixWithCalibrationFactor<Cal3Bundler>>(1, 2, pA(i), pB(i), model1);
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graph.emplace_shared<EssentialMatrixWithCalibrationFactor<Cal3_S2>>(
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1, 2, pA(i), pB(i), model1);
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// Check error at ground truth
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Values truth;
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@ -229,16 +233,17 @@ TEST (EssentialMatrixWithCalibrationFactor, minimization) {
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// Check error at initial estimate
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Values initial;
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EssentialMatrix initialE = trueE.retract(
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(Vector(5) << 0.1, -0.1, 0.1, 0.1, -0.1).finished());
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Cal3Bundler initialK = trueK.retract(
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(Vector(3) << 0.1, -1e-1, 3e-2).finished());
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EssentialMatrix initialE =
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trueE.retract((Vector(5) << 0.1, -0.1, 0.1, 0.1, -0.1).finished());
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Cal3_S2 initialK =
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trueK.retract((Vector(5) << 0.1, -0.1, 0.03, -0.2, 0.2).finished());
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initial.insert(1, initialE);
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initial.insert(2, initialK);
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initial.insert(2, trueK);
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#if defined(GTSAM_ROT3_EXPMAP) || defined(GTSAM_USE_QUATERNIONS)
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EXPECT_DOUBLES_EQUAL(618.94, graph.error(initial), 1e-2);
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EXPECT_DOUBLES_EQUAL(643.26, graph.error(initial), 1e-2);
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#else
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EXPECT_DOUBLES_EQUAL(639.84, graph.error(initial), 1e-2);
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EXPECT_DOUBLES_EQUAL(639.84, graph.error(initial),
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1e-2); // TODO: update this value too
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#endif
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// Optimize
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@ -248,20 +253,20 @@ TEST (EssentialMatrixWithCalibrationFactor, minimization) {
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// Check result
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EssentialMatrix actualE = result.at<EssentialMatrix>(1);
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Cal3Bundler actualK = result.at<Cal3Bundler>(2);
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EXPECT(assert_equal(trueE, actualE, 1e-1));
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EXPECT(assert_equal(trueK, actualK, 1e-1));
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Cal3_S2 actualK = result.at<Cal3_S2>(2);
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EXPECT(assert_equal(trueE, actualE, 1e-2));
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EXPECT(assert_equal(trueK, actualK, 1e-2));
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// Check error at result
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EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-4);
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// Check errors individually
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for (size_t i = 0; i < 5; i++)
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EXPECT_DOUBLES_EQUAL(0, actualE.error(vA(i, actualK), vB(i, actualK)), 1e-6);
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EXPECT_DOUBLES_EQUAL(0, actualE.error(vA(i, actualK), vB(i, actualK)),
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1e-6);
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}
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} // namespace example1
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} // namespace example1
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//*************************************************************************
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@ -283,9 +288,9 @@ TEST (EssentialMatrixWithCalibrationFactor, minimization) {
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// return data.tracks[i].measurements[1].second;
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// }
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// boost::shared_ptr<Cal3Bundler> //
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// K = boost::make_shared<Cal3Bundler>(500, 0, 0);
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// PinholeCamera<Cal3Bundler> camera2(data.cameras[1].pose(), *K);
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// boost::shared_ptr<Cal3_S2> //
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// K = boost::make_shared<Cal3_S2>(500, 0, 0);
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// PinholeCamera<Cal3_S2> camera2(data.cameras[1].pose(), *K);
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||||
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||||
// Vector vA(size_t i) {
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// Point2 xy = K->calibrate(pA(i));
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|
|
@ -302,7 +307,8 @@ TEST (EssentialMatrixWithCalibrationFactor, minimization) {
|
|||
|
||||
// NonlinearFactorGraph graph;
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||||
// for (size_t i = 0; i < 5; i++)
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||||
// graph.emplace_shared<EssentialMatrixWithCalibrationFactor>(1, pA(i), pB(i), model1, K);
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||||
// graph.emplace_shared<EssentialMatrixWithCalibrationFactor>(1, pA(i),
|
||||
// pB(i), model1, K);
|
||||
|
||||
// // Check error at ground truth
|
||||
// Values truth;
|
||||
|
|
@ -359,8 +365,9 @@ TEST (EssentialMatrixWithCalibrationFactor, minimization) {
|
|||
// boost::function<Vector(const EssentialMatrix&, double)> f = boost::bind(
|
||||
// &EssentialMatrixFactor2::evaluateError, &factor, _1, _2, boost::none,
|
||||
// boost::none);
|
||||
// Hexpected1 = numericalDerivative21<Vector2, EssentialMatrix, double>(f, trueE, d);
|
||||
// Hexpected2 = numericalDerivative22<Vector2, EssentialMatrix, double>(f, trueE, d);
|
||||
// Hexpected1 = numericalDerivative21<Vector2, EssentialMatrix, double>(f,
|
||||
// trueE, d); Hexpected2 = numericalDerivative22<Vector2, EssentialMatrix,
|
||||
// double>(f, trueE, d);
|
||||
|
||||
// // Verify the Jacobian is correct
|
||||
// EXPECT(assert_equal(Hexpected1, Hactual1, 1e-6));
|
||||
|
|
@ -376,7 +383,8 @@ TEST (EssentialMatrixWithCalibrationFactor, minimization) {
|
|||
// // Noise sigma is 1, assuming pixel measurements
|
||||
// NonlinearFactorGraph graph;
|
||||
// for (size_t i = 0; i < data.number_tracks(); i++)
|
||||
// graph.emplace_shared<EssentialMatrixFactor2>(100, i, pA(i), pB(i), model2, K);
|
||||
// graph.emplace_shared<EssentialMatrixFactor2>(100, i, pA(i), pB(i),
|
||||
// model2, K);
|
||||
|
||||
// // Check error at ground truth
|
||||
// Values truth;
|
||||
|
|
@ -427,8 +435,9 @@ TEST (EssentialMatrixWithCalibrationFactor, minimization) {
|
|||
// boost::function<Vector(const EssentialMatrix&, double)> f = boost::bind(
|
||||
// &EssentialMatrixFactor3::evaluateError, &factor, _1, _2, boost::none,
|
||||
// boost::none);
|
||||
// Hexpected1 = numericalDerivative21<Vector2, EssentialMatrix, double>(f, bodyE, d);
|
||||
// Hexpected2 = numericalDerivative22<Vector2, EssentialMatrix, double>(f, bodyE, d);
|
||||
// Hexpected1 = numericalDerivative21<Vector2, EssentialMatrix, double>(f,
|
||||
// bodyE, d); Hexpected2 = numericalDerivative22<Vector2, EssentialMatrix,
|
||||
// double>(f, bodyE, d);
|
||||
|
||||
// // Verify the Jacobian is correct
|
||||
// EXPECT(assert_equal(Hexpected1, Hactual1, 1e-6));
|
||||
|
|
|
|||
Loading…
Reference in New Issue