chessboard_stereo_camera_calib.cpp

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/* +---------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)               |
   |                          http://www.mrpt.org/                             |
   |                                                                           |
   | Copyright (c) 2005-2017, Individual contributors, see AUTHORS file        |
   | See: http://www.mrpt.org/Authors - All rights reserved.                   |
   | Released under BSD License. See details in http://www.mrpt.org/License    |
   +---------------------------------------------------------------------------+ */
 
#include "vision-precomp.h"   // Precompiled headers
 
#include <mrpt/system/filesystem.h>
#include <mrpt/utils/CConfigFileMemory.h>
 
#include <mrpt/vision/chessboard_find_corners.h>
#include <mrpt/vision/chessboard_stereo_camera_calib.h>
#include <mrpt/vision/pinhole.h>
#include <mrpt/math/robust_kernels.h>
#include <mrpt/math/wrap2pi.h>
#include <algorithm> // reverse()
 
#include "chessboard_stereo_camera_calib_internal.h"
 
//#define USE_NUMERIC_JACOBIANS
//#define COMPARE_NUMERIC_JACOBIANS
 
#ifdef USE_NUMERIC_JACOBIANS
#       include <mrpt/math/jacobians.h>
#endif
 
using namespace mrpt;
using namespace mrpt::vision;
using namespace mrpt::utils;
using namespace mrpt::poses;
using namespace mrpt::math;
using namespace std;
 
 
 
/* -------------------------------------------------------
                                checkerBoardStereoCalibration
   ------------------------------------------------------- */
bool mrpt::vision::checkerBoardStereoCalibration(
        TCalibrationStereoImageList & images,
        const TStereoCalibParams    & p,
        TStereoCalibResults         & out
        )
{
        try
        {
                ASSERT_(p.check_size_x>2)
                ASSERT_(p.check_size_y>2)
                ASSERT_(p.check_squares_length_X_meters>0)
                ASSERT_(p.check_squares_length_Y_meters>0)
 
                const bool user_wants_use_robust = p.use_robust_kernel;
 
                if (images.size()<1)
                {
                        std::cout << "ERROR: No input images." << std::endl;
                        return false;
                }
 
                const unsigned   CORNERS_COUNT = p.check_size_x * p.check_size_y;
                const TImageSize check_size    = TImageSize(p.check_size_x, p.check_size_y);
                TImageStereoCallbackData cbPars;
 
                // For each image, find checkerboard corners:
                // -----------------------------------------------
                // Left/Right sizes (may be different)
        TImageSize imgSize[2] = { TImageSize(0,0), TImageSize(0,0) };
 
                vector<size_t>   valid_image_pair_indices; // Indices in images[] which are valid pairs to be used in the optimization
 
                for (size_t i=0;i<images.size();i++)
                {
                        // Do loop for each left/right image:
                        TImageCalibData *dats[2] = { &images[i].left, &images[i].right };
                        bool corners_found[2]={ false, false };
 
                        for (int lr=0;lr<2;lr++)
                        {
                                TImageCalibData &dat = *dats[lr];
                                dat.detected_corners.clear();
 
                                // Make grayscale version:
                                const CImage img_gray( dat.img_original, FAST_REF_OR_CONVERT_TO_GRAY );
 
                                if (!i)
                                {
                                        imgSize[lr] = img_gray.getSize();
                                        if (lr==0) {
                                                out.cam_params.leftCamera.ncols = imgSize[lr].x;
                                                out.cam_params.leftCamera.nrows = imgSize[lr].y;
                                        }
                                        else {
                                                out.cam_params.rightCamera.ncols = imgSize[lr].x;
                                                out.cam_params.rightCamera.nrows = imgSize[lr].y;
                                        }
                                }
                                else
                                {
                                        if (imgSize[lr].y != (int)img_gray.getHeight() || imgSize[lr].x != (int)img_gray.getWidth())
                                        {
                                                std::cout << "ERROR: All the images in each left/right channel must have the same size." << std::endl;
                                                return false;
                                        }
                                }
 
                                // Do detection (this includes the "refine corners" with cvFindCornerSubPix):
                                corners_found[lr] = mrpt::vision::findChessboardCorners(
                                        img_gray,
                                        dat.detected_corners,
                                        p.check_size_x,p.check_size_y,
                                        p.normalize_image // normalize_image
                                        );
 
                                if (corners_found[lr] && dat.detected_corners.size()!=CORNERS_COUNT)
                                        corners_found[lr] = false;
 
                                if (p.verbose) cout << format("%s img #%u: %s\n", lr==0 ? "LEFT":"RIGHT", static_cast<unsigned int>(i), corners_found[lr] ? "DETECTED" : "NOT DETECTED" );
                                // User Callback?
                                if (p.callback)
                                {
                                        cbPars.calibRound = -1; // Detecting corners
                                        cbPars.current_iter = 0;
                                        cbPars.current_rmse = 0;
                                        cbPars.nImgsProcessed = i*2 + lr+1;
                                        cbPars.nImgsToProcess = images.size()*2;
                                        (*p.callback)(cbPars, p.callback_user_param);
                                }
 
                                if( corners_found[lr] )
                                {
                                        // Draw the checkerboard in the corresponding image:
                                        if ( !dat.img_original.isExternallyStored() )
                                        {
                                                // Checkboad as color image:
                                                dat.img_original.colorImage( dat.img_checkboard );
                                                dat.img_checkboard.drawChessboardCorners( dat.detected_corners,check_size.x,check_size.y);
                                        }
                                }
 
                        } // end for lr
 
                        // We just finished detecting corners in a left/right pair.
                        // Only if corners were detected perfectly in BOTH images, add this image pair as a good one for optimization:
                        if( corners_found[0] && corners_found[1] )
                        {
                                valid_image_pair_indices.push_back(i);
 
                                // Consistency between left/right pair: the corners MUST BE ORDERED so they match to each other,
                                // and the criterion must be the same in ALL pairs to avoid rubbish optimization:
 
                                // Key idea: Generate a representative vector that goes along rows and columns.
                                // Check the angle of those director vectors between L/R images. That can be done
                                // via the dot product. Swap rows/columns order as needed.
                                bool has_to_redraw_corners = false;
 
                                const mrpt::math::TPoint2D
                                        pt_l0 = images[i].left.detected_corners[0],
                                        pt_l1 = images[i].left.detected_corners[1],
                                        pt_r0 = images[i].right.detected_corners[0],
                                        pt_r1 = images[i].right.detected_corners[1];
                                const mrpt::math::TPoint2D Al = pt_l1 - pt_l0;
                                const mrpt::math::TPoint2D Ar = pt_r1 - pt_r0;
 
                                // If the dot product is negative, we have INVERTED order of corners:
                                if (Al.x*Ar.x+Al.y*Ar.y < 0)
                                {
                                        has_to_redraw_corners = true;
                                        // Invert all corners:
                                        std::reverse( images[i].right.detected_corners.begin(), images[i].right.detected_corners.end() );
                                }
 
 
                                if (has_to_redraw_corners)
                                {
                                        // Checkboad as color image:
                                        images[i].right.img_original.colorImage( images[i].right.img_checkboard );
                                        images[i].right.img_checkboard.drawChessboardCorners( images[i].right.detected_corners,check_size.x,check_size.y);
                                }
                        }
 
                } // end find corners
 
                if (p.verbose) std::cout << valid_image_pair_indices.size() << " valid image pairs." << std::endl;
                if (valid_image_pair_indices.empty() )
                {
                        std::cout << "ERROR: No valid images. Perhaps the checkerboard size is incorrect?" << std::endl;
                        return false;
                }
 
                //  Create reference coordinates of the detected corners, in "global" coordinates:
                // -------------------------------------------------------------------------------
                vector<TPoint3D> obj_points;
                obj_points.reserve(CORNERS_COUNT);
                for( unsigned int y = 0; y < p.check_size_y; y++ )
                        for( unsigned int x = 0; x < p.check_size_x; x++  )
                                obj_points.push_back( TPoint3D(
                                        p.check_squares_length_X_meters * x,
                                        p.check_squares_length_Y_meters * y,
                                        0 ));
 
 
                // ----------------------------------------------------------------------------------
                //  Levenberg-Marquardt algorithm
                //
                //  State space (poses are actually on-manifold increments):
                //     N*left_cam_pose + right2left_pose + left_cam_params + right_cam_params
                //       N * 6         +        6        +        9        +         9       = 6N+24
                // ----------------------------------------------------------------------------------
                const size_t N = valid_image_pair_indices.size();
                const size_t nObs = 2 * N * CORNERS_COUNT;  // total number of valid observations (px,py) taken into account in the optimization
 
                const double tau = 0.3;
                const double t1 = 1e-8;
                const double t2 = 1e-8;
                const double MAX_LAMBDA = 1e20;
 
                // Initial state:
                //Within lm_stat: CArrayDouble<9> left_cam_params, right_cam_params; // [fx fy cx cy k1 k2 k3 t1 t2]
                lm_stat_t        lm_stat(images,valid_image_pair_indices,obj_points);
 
                lm_stat.left_cam_params[0] = imgSize[0].x * 0.9;
                lm_stat.left_cam_params[1] = imgSize[0].y * 0.9;
                lm_stat.left_cam_params[2] = imgSize[0].x * 0.5;
                lm_stat.left_cam_params[3] = imgSize[0].y * 0.5;
                lm_stat.left_cam_params[4] = lm_stat.left_cam_params[5] = lm_stat.left_cam_params[6] =
                lm_stat.left_cam_params[7] = lm_stat.left_cam_params[8] = 0;
 
                lm_stat.right_cam_params[0] = imgSize[1].x * 0.9;
                lm_stat.right_cam_params[1] = imgSize[1].y * 0.9;
                lm_stat.right_cam_params[2] = imgSize[1].x * 0.5;
                lm_stat.right_cam_params[3] = imgSize[1].y * 0.5;
                lm_stat.right_cam_params[4] = lm_stat.right_cam_params[5] = lm_stat.right_cam_params[6] =
                lm_stat.right_cam_params[7] = lm_stat.right_cam_params[8] = 0;
 
 
                // ===============================================================================
                //   Run stereo calibration in two stages:
                //   (0) Estimate all parameters except distortion
                //   (1) Estimate all parameters, using as starting point the guess from (0)
                // ===============================================================================
                size_t nUnknownsCamParams=0;
                size_t iter=0;
                double err=0;
                vector_size_t  vars_to_optimize;
                Eigen::MatrixXd  H;       // Hessian matrix  (Declared here so it's accessible as the final uncertainty measure)
 
                for (int calibRound=0; calibRound<2;calibRound++)
                {
                        cbPars.calibRound = calibRound;
                        if (p.verbose)
                                cout << ((calibRound==0) ?
                                "LM calibration round #0: WITHOUT distortion ----------\n"
                                :
                                "LM calibration round #1: ALL parameters --------------\n");
 
                        // Select which cam. parameters to optimize (for each camera!) and which to leave fixed:
                        // Indices:  [0   1  2  3  4  5  6  7  8]
                        // Variables:[fx fy cx cy k1 k2 k3 t1 t2]
                        vars_to_optimize.clear();
                        vars_to_optimize.push_back(0);
                        vars_to_optimize.push_back(1);
                        vars_to_optimize.push_back(2);
                        vars_to_optimize.push_back(3);
                        if (calibRound==1)
                        {
                                if (p.optimize_k1) vars_to_optimize.push_back(4);
                                if (p.optimize_k2) vars_to_optimize.push_back(5);
                                if (p.optimize_t1) vars_to_optimize.push_back(6);
                                if (p.optimize_t2) vars_to_optimize.push_back(7);
                                if (p.optimize_k3) vars_to_optimize.push_back(8);
                        }
 
                        nUnknownsCamParams = vars_to_optimize.size();
 
                        //  * N x Manifold Epsilon of left camera pose (6)
                        //  * Manifold Epsilon of right2left pose (6)
                        //  * Left-cam-params (<=9)
                        //  * Right-cam-params (<=9)
                        const size_t nUnknowns = N*6 + 6+ 2*nUnknownsCamParams;
 
                        // Residuals & Jacobians:
                        TResidualJacobianList res_jacob;
                        err = recompute_errors_and_Jacobians(lm_stat,res_jacob, false /* no robust */, p.robust_kernel_param);
 
                        // Build linear system:
                        Eigen::VectorXd  minus_g; // minus gradient
                        build_linear_system(res_jacob,vars_to_optimize,minus_g,H);
 
                        ASSERT_EQUAL_(nUnknowns,(size_t) H.cols())
 
                        // Lev-Marq. parameters:
                        double nu = 2;
                        double lambda = tau * H.diagonal().array().maxCoeff();
                        bool   done = (minus_g.array().abs().maxCoeff() < t1);
                        int    numItersImproving = 0;
                        bool   use_robust = false;
 
                        // Lev-Marq. main loop:
                        iter=0;
                        while (iter<p.maxIters && !done)
                        {
                                // User Callback?
                                if (p.callback)
                                {
                                        cbPars.current_iter = iter;
                                        cbPars.current_rmse = std::sqrt(err/nObs);
                                        (*p.callback)(cbPars, p.callback_user_param);
                                }
 
                                // Solve for increment: (H + \lambda I) eps = -gradient
                                Eigen::MatrixXd  HH = H;
                                for (size_t i=0;i<nUnknowns;i++)
                                        HH(i,i)+=lambda;
                                        //HH(i,i)*= (1.0 + lambda);
 
                                Eigen::LLT<Eigen::MatrixXd> llt( HH.selfadjointView<Eigen::Lower>() );
                                if (llt.info()!=Eigen::Success)
                                {
                                        lambda *= nu;
                                        nu *= 2;
                                        done = (lambda>MAX_LAMBDA);
 
                                        if (p.verbose && !done) cout << "LM iter#"<<iter<<": Couldn't solve LLt, retrying with larger lambda=" << lambda << endl;
                                        continue;
                                }
                                const Eigen::VectorXd eps = llt.solve(minus_g);
 
                                const double eps_norm = eps.norm();
                                if (eps_norm < t2*(eps_norm+t2))
                                {
                                        if (p.verbose) cout << "Termination criterion: eps_norm < t2*(eps_norm+t2): "<<eps_norm << " < " << t2*(eps_norm+t2) <<endl;
                                        done=true;
                                        break;
                                }
 
                                // Tentative new state:
                                lm_stat_t new_lm_stat(lm_stat); //images,valid_image_pair_indices,obj_points);
                                add_lm_increment(eps,vars_to_optimize, new_lm_stat);
 
                                TResidualJacobianList new_res_jacob;
 
                                // discriminant:
                                double err_new = recompute_errors_and_Jacobians(new_lm_stat, new_res_jacob, use_robust, p.robust_kernel_param);
 
                                if(err_new<err)
                                {
                                        //const double l = (err-err_new)/ (eps.dot( lambda*eps + minus_g) );
 
                                        if (numItersImproving++>5)
                                                use_robust  = user_wants_use_robust;
 
                                        // Good: Accept new values
                                        if (p.verbose) cout << "LM iter#"<<iter<<": Avr.err(px):"<< std::sqrt(err/nObs)  <<"->" <<  std::sqrt(err_new/nObs) << " lambda:" << lambda << endl;
 
                                        // swap: faster than "lm_stat <- new_lm_stat"
                                        lm_stat.swap( new_lm_stat );
                                        res_jacob.swap( new_res_jacob );
 
                                        err = err_new;
                                        build_linear_system(res_jacob,vars_to_optimize,minus_g,H);
 
                                        // Too small gradient?
                                        done = (minus_g.array().abs().maxCoeff() < t1);
                                        //lambda *= max(1.0/3.0, 1-std::pow(2*l-1,3.0) );
                                        lambda*=0.6;
                                        lambda = std::max(lambda, 1e-100);
                                        nu = 2.0;
 
                                        iter++;
                                }
                                else
                                {
                                        lambda *= nu;
                                        if (p.verbose) cout << "LM iter#"<<iter<<": No update: err=" << err << " -> err_new=" << err_new << " retrying with larger lambda=" << lambda << endl;
                                        nu *= 2.0;
                                        done = (lambda>MAX_LAMBDA);
                                }
 
                        } // end while LevMarq.
 
                } // end for each calibRound = [0,1]
                // -------------------------------------------------------------------------------
                //  End of Levenberg-Marquardt
                // -------------------------------------------------------------------------------
 
                // Save final optimum values to the output structure
                out.final_rmse  = std::sqrt(err/nObs);
                out.final_iters = iter;
                out.final_number_good_image_pairs = N;
 
                // [fx fy cx cy k1 k2 k3 t1 t2]
                out.cam_params.leftCamera.fx( lm_stat.left_cam_params[0] );
                out.cam_params.leftCamera.fy( lm_stat.left_cam_params[1] );
                out.cam_params.leftCamera.cx( lm_stat.left_cam_params[2] );
                out.cam_params.leftCamera.cy( lm_stat.left_cam_params[3] );
                out.cam_params.leftCamera.k1( lm_stat.left_cam_params[4] );
                out.cam_params.leftCamera.k2( lm_stat.left_cam_params[5] );
                out.cam_params.leftCamera.k3( lm_stat.left_cam_params[6] );
                out.cam_params.leftCamera.p1( lm_stat.left_cam_params[7] );
                out.cam_params.leftCamera.p2( lm_stat.left_cam_params[8] );
 
 
                // [fx fy cx cy k1 k2 k3 t1 t2]
                out.cam_params.rightCamera.fx( lm_stat.right_cam_params[0] );
                out.cam_params.rightCamera.fy( lm_stat.right_cam_params[1] );
                out.cam_params.rightCamera.cx( lm_stat.right_cam_params[2] );
                out.cam_params.rightCamera.cy( lm_stat.right_cam_params[3] );
                out.cam_params.rightCamera.k1( lm_stat.right_cam_params[4] );
                out.cam_params.rightCamera.k2( lm_stat.right_cam_params[5] );
                out.cam_params.rightCamera.k3( lm_stat.right_cam_params[6] );
                out.cam_params.rightCamera.p1( lm_stat.right_cam_params[7] );
                out.cam_params.rightCamera.p2( lm_stat.right_cam_params[8] );
 
                // R2L pose:
                out.right2left_camera_pose = lm_stat.right2left_pose;
 
                // All the estimated camera poses:
                out.left_cam_poses = lm_stat.left_cam_poses;
 
                out.image_pair_was_used.assign(images.size(), false);
                for (size_t i=0;i<valid_image_pair_indices.size();i++)
                        out.image_pair_was_used[valid_image_pair_indices[i]]=true;
 
                // Uncertainties ---------------------
                // The order of inv. variances in the diagonal of the Hessian is:
                //  * N x Manifold Epsilon of left camera pose (6)
                //  * Manifold Epsilon of right2left pose (6)
                //  * Left-cam-params (<=9)
                //  * Right-cam-params (<=9)
                out.left_params_inv_variance.setConstant(0);
                out.right_params_inv_variance.setConstant(0);
                const size_t base_idx_H_CPs = H.cols()-2*nUnknownsCamParams;
                for (size_t i=0;i<nUnknownsCamParams;i++)
                {
                        out.left_params_inv_variance [ vars_to_optimize[i] ] = H(base_idx_H_CPs+i,base_idx_H_CPs+i);
                        out.right_params_inv_variance[ vars_to_optimize[i] ] = H(base_idx_H_CPs+nUnknownsCamParams+i,base_idx_H_CPs+nUnknownsCamParams+i);
                }
 
                // Draw projected points
                for (size_t i=0;i<valid_image_pair_indices.size();i++)
                {
                        //mrpt::poses::CPose3D                  reconstructed_camera_pose;   //!< At output, the reconstructed pose of the camera.
                        //std::vector<TPixelCoordf>             projectedPoints_distorted;   //!< At output, only will have an empty vector if the checkerboard was not found in this image, or the predicted (reprojected) corners, which were used to estimate the average square error.
                        //std::vector<TPixelCoordf>             projectedPoints_undistorted; //!< At output, like projectedPoints_distorted but for the undistorted image.
                        const size_t idx = valid_image_pair_indices[i];
 
                        TImageCalibData &dat_l = images[idx].left;
                        TImageCalibData &dat_r = images[idx].right;
 
                        // Rectify image.
                        dat_l.img_original.colorImage( dat_l.img_rectified );
                        dat_r.img_original.colorImage( dat_r.img_rectified );
 
                        // Camera poses:
                        dat_l.reconstructed_camera_pose = - lm_stat.left_cam_poses[idx];
                        dat_r.reconstructed_camera_pose = -(lm_stat.right2left_pose + lm_stat.left_cam_poses[idx]);
 
                        // Project distorted images:
                        mrpt::vision::pinhole::projectPoints_with_distortion(obj_points, out.cam_params.leftCamera,  CPose3DQuat(dat_l.reconstructed_camera_pose), dat_l.projectedPoints_distorted, true);
                        mrpt::vision::pinhole::projectPoints_with_distortion(obj_points, out.cam_params.rightCamera, CPose3DQuat(dat_r.reconstructed_camera_pose), dat_r.projectedPoints_distorted, true);
 
                        // Draw corners:
                        dat_l.img_rectified.drawChessboardCorners( dat_l.projectedPoints_distorted, check_size.x,check_size.y);
                        dat_r.img_rectified.drawChessboardCorners( dat_r.projectedPoints_distorted, check_size.x,check_size.y);
                }
 
                return true;
        }
        catch(std::exception &e)
        {
                std::cout << e.what() << std::endl;
                return false;
        }
}
 
/*
Jacobian:
 
 d b( [px py pz] )
------------------  (5x3) =
  d{ px py pz }
 
\left(\begin{array}{ccc} \frac{1}{\mathrm{pz}} & 0 & -\frac{\mathrm{px}}{{\mathrm{pz}}^2}\\ 0 & \frac{1}{\mathrm{pz}} & -\frac{\mathrm{py}}{{\mathrm{pz}}^2} \end{array}\right)
 
*/
 
void jacob_db_dp(
        const TPoint3D &p,   // 3D coordinates wrt the camera
        Eigen::Matrix<double,2,3> &G)
{
        const double pz_ = 1/p.z;
        const double pz_2 = pz_*pz_;
 
        G(0,0) = pz_;
        G(0,1) = 0;
        G(0,2) = -p.x * pz_2;
 
        G(1,0) = 0;
        G(1,1) = pz_;
        G(1,2) = -p.y * pz_2;
}
 
/*
Jacobian:
 
 dh( b,c )
----------- = Hb  | 2x2  (b=[x=px/pz y=py/pz])
  d{ b }
 
\left(\begin{array}{cc} \mathrm{fx}\, \left(\mathrm{k2}\, {\left(x^2 + y^2\right)}^2 + \mathrm{k3}\, {\left(x^2 + y^2\right)}^3 + 6\, \mathrm{t2}\, x + 2\, \mathrm{t1}\, y + x\, \left(2\, \mathrm{k1}\, x + 4\, \mathrm{k2}\, x\, \left(x^2 + y^2\right) + 6\, \mathrm{k3}\, x\, {\left(x^2 + y^2\right)}^2\right) + \mathrm{k1}\, \left(x^2 + y^2\right) + 1\right) & \mathrm{fx}\, \left(2\, \mathrm{t1}\, x + 2\, \mathrm{t2}\, y + x\, \left(2\, \mathrm{k1}\, y + 4\, \mathrm{k2}\, y\, \left(x^2 + y^2\right) + 6\, \mathrm{k3}\, y\, {\left(x^2 + y^2\right)}^2\right)\right)\\ \mathrm{fy}\, \left(2\, \mathrm{t1}\, x + 2\, \mathrm{t2}\, y + y\, \left(2\, \mathrm{k1}\, x + 4\, \mathrm{k2}\, x\, \left(x^2 + y^2\right) + 6\, \mathrm{k3}\, x\, {\left(x^2 + y^2\right)}^2\right)\right) & \mathrm{fy}\, \left(\mathrm{k2}\, {\left(x^2 + y^2\right)}^2 + \mathrm{k3}\, {\left(x^2 + y^2\right)}^3 + 2\, \mathrm{t2}\, x + 6\, \mathrm{t1}\, y + y\, \left(2\, \mathrm{k1}\, y + 4\, \mathrm{k2}\, y\, \left(x^2 + y^2\right) + 6\, \mathrm{k3}\, y\, {\left(x^2 + y^2\right)}^2\right) + \mathrm{k1}\, \left(x^2 + y^2\right) + 1\right) \end{array}\right)
 
 dh( b,c )
----------- = Hc  | 2x9
  d{ c }
 
\left(\begin{array}{ccccccccc} \mathrm{t2}\, \left(3\, x^2 + y^2\right) + x\, \left(\mathrm{k2}\, {\left(x^2 + y^2\right)}^2 + \mathrm{k3}\, {\left(x^2 + y^2\right)}^3 + \mathrm{k1}\, \left(x^2 + y^2\right) + 1\right) + 2\, \mathrm{t1}\, x\, y & 0 & 1 & 0 & \mathrm{fx}\, x\, \left(x^2 + y^2\right) & \mathrm{fx}\, x\, {\left(x^2 + y^2\right)}^2 & \mathrm{fx}\, x\, {\left(x^2 + y^2\right)}^3 & 2\, \mathrm{fx}\, x\, y & \mathrm{fx}\, \left(3\, x^2 + y^2\right)\\ 0 & \mathrm{t1}\, \left(x^2 + 3\, y^2\right) + y\, \left(\mathrm{k2}\, {\left(x^2 + y^2\right)}^2 + \mathrm{k3}\, {\left(x^2 + y^2\right)}^3 + \mathrm{k1}\, \left(x^2 + y^2\right) + 1\right) + 2\, \mathrm{t2}\, x\, y & 0 & 1 & \mathrm{fy}\, y\, \left(x^2 + y^2\right) & \mathrm{fy}\, y\, {\left(x^2 + y^2\right)}^2 & \mathrm{fy}\, y\, {\left(x^2 + y^2\right)}^3 & \mathrm{fy}\, \left(x^2 + 3\, y^2\right) & 2\, \mathrm{fy}\, x\, y \end{array}\right)
 
*/
 
void jacob_dh_db_and_dh_dc(
        const TPoint3D & nP,  // Point in relative coords wrt the camera
        const Eigen::Matrix<double,9,1>  & c,  // camera parameters
        Eigen::Matrix<double,2,2>  & Hb,
        Eigen::Matrix<double,2,9> & Hc
        )
{
        const double x = nP.x/nP.z;
        const double y = nP.y/nP.z;
 
        const double r2   = x*x + y*y;
        const double r    = std::sqrt(r2);
        const double r6   = r2*r2*r2; // (x^2+y^2)^3 = r^6
 
        // c=[fx fy cx cy k1 k2 k3 t1 t2]
        const double fx=c[0], fy=c[1];
        //const double cx=c[2], cy=c[3]; // Un-unused
        const double k1=c[4], k2=c[5], k3=c[6];
        const double t1=c[7], t2=c[8];
 
        // Hb = dh(b,c)/db  (2x2)
        Hb(0,0) = fx*(k2*r2 + k3*r6 + 6*t2*x + 2*t1*y + x*(2*k1*x + 4*k2*x*r + 6*k3*x*r2) + k1*r + 1);
        Hb(0,1) = fx*(2*t1*x + 2*t2*y + x*(2*k1*y + 4*k2*y*r + 6*k3*y*r2));
 
        Hb(1,0) = fy*(2*t1*x + 2*t2*y + y*(2*k1*x + 4*k2*x*r + 6*k3*x*r2));
        Hb(1,1) = fy*(k2*r2 + k3*r6 + 2*t2*x + 6*t1*y + y*(2*k1*y + 4*k2*y*r + 6*k3*y*r2) + k1*r + 1);
 
 
        // Hc = dh(b,c)/dc  (2x9)
        Hc(0,0) = t2*(3*x*x + y*y) + x*(k2*r2 + k3*r6 + k1*r + 1) + 2*t1*x*y;
        Hc(0,1) = 0;
        Hc(0,2) = 1;
        Hc(0,3) = 0;
        Hc(0,4) = fx*x*r;
        Hc(0,5) = fx*x*r2;
        Hc(0,6) = fx*x*r6;
        Hc(0,7) = 2*fx*x*y;
        Hc(0,8) = fx*(3*x*x + y*y);
 
        Hc(1,0) = 0;
        Hc(1,1) = t1*(x*x + 3*y*y) + y*(k2*r2 + k3*r6 + k1*r + 1) + 2*t2*x*y;
        Hc(1,2) = 0;
        Hc(1,3) = 1;
        Hc(1,4) = fy*y*r;
        Hc(1,5) = fy*y*r2;
        Hc(1,6) = fy*y*r6;
        Hc(1,7) = fy*(x*x + 3*y*y);
        Hc(1,8) = 2*fy*x*y;
 
}
 
 
void jacob_deps_D_p_deps(
        const TPoint3D &p_D,   // D (+) p
        Eigen::Matrix<double,3,6> &dpl_del)
{
        // Jacobian 10.3.4 in technical report "A tutorial on SE(3) transformation parameterizations and on-manifold optimization"
        dpl_del.block<3,3>(0,0).setIdentity();
        dpl_del.block<3,3>(0,3) = mrpt::math::skew_symmetric3_neg(p_D);
}
 
void jacob_dA_eps_D_p_deps(
        const CPose3D  &A,
        const CPose3D  &D,
        const TPoint3D &p,
        Eigen::Matrix<double,3,6> &dp_deps)
{
        // Jacobian 10.3.7 in technical report "A tutorial on SE(3) transformation parameterizations and on-manifold optimization"
        const Eigen::Matrix<double,1,3> P(p.x,p.y,p.z);
        const Eigen::Matrix<double,1,3> dr1 = D.getRotationMatrix().block<1,3>(0,0);
        const Eigen::Matrix<double,1,3> dr2 = D.getRotationMatrix().block<1,3>(1,0);
        const Eigen::Matrix<double,1,3> dr3 = D.getRotationMatrix().block<1,3>(2,0);
 
        const Eigen::Matrix<double,1,3> v( P.dot(dr1)+D.x(), P.dot(dr2)+D.y(), P.dot(dr3)+D.z() );
 
        Eigen::Matrix<double,3,6> H;
        H.block<3,3>(0,0).setIdentity();
        H.block<3,3>(0,3) = mrpt::math::skew_symmetric3_neg(v);
 
        dp_deps.noalias() = A.getRotationMatrix() * H;
}
 
 
void project_point(
        const mrpt::math::TPoint3D & P,
        const mrpt::utils::TCamera & params,
        const CPose3D              & cameraPose,
        mrpt::utils::TPixelCoordf & px
        )
{
        // Change the reference system to that wrt the camera
        TPoint3D nP;
        cameraPose.composePoint( P.x, P.y, P.z, nP.x, nP.y, nP.z );
 
        // Pinhole model:
        const double x = nP.x/nP.z;
        const double y = nP.y/nP.z;
 
        // Radial distortion:
        const double r2 = square(x)+square(y);
        const double r4 = square(r2);
        const double r6 = r2*r4;
        const double A  = 1+params.dist[0]*r2+params.dist[1]*r4+params.dist[4]*r6;
        const double B  = 2*x*y;
 
        px.x = params.cx() + params.fx() * ( x*A + params.dist[2]*B + params.dist[3]*(r2+2*square(x)) );
        px.y = params.cy() + params.fy() * ( y*A + params.dist[3]*B + params.dist[2]*(r2+2*square(y)) );
}
 
// Build the "-gradient" and the Hessian matrix:
// Variables are in these order:
//  * N x Manifold Epsilon of left camera pose (6)
//  * Manifold Epsilon of right2left pose (6)
//  * Left-cam-params (<=9)
//  * Right-cam-params (<=9)
void mrpt::vision::build_linear_system(
        const TResidualJacobianList  & res_jac,
        const vector_size_t & var_indxs,
        Eigen::VectorXd &minus_g,
        Eigen::MatrixXd &H)
{
        const size_t N = res_jac.size();  // Number of stereo image pairs
        const size_t nMaxUnknowns = N*6+6+9+9;
 
        // Reset to zeros:
        Eigen::VectorXd minus_g_tot = Eigen::VectorXd::Zero(nMaxUnknowns);
        Eigen::MatrixXd H_tot       = Eigen::MatrixXd::Zero(nMaxUnknowns,nMaxUnknowns);
 
        // Sum the contribution from each observation:
        for (size_t i=0;i<N;i++)
        {
                const size_t nObs = res_jac[i].size();
                for (size_t j=0;j<nObs;j++)
                {
                        const TResidJacobElement & rje = res_jac[i][j];
                        // Sub-Hessian & sub-gradient are variables in this order:
                        //  eps_left_cam, eps_right2left_cam, left_cam_params, right_cam_params
                        const Eigen::Matrix<double,30,30> Hij = rje.J.transpose() * rje.J;
                        const Eigen::Matrix<double,30,1>  gij = rje.J.transpose() * rje.residual;
 
                        // Assemble in their place:
                        minus_g_tot.block<6,1>(i*6,0)     -= gij.block<6,1>(0,0);
                        minus_g_tot.block<6+9+9,1>(N*6,0) -= gij.block<6+9+9,1>(6,0);
 
                        H_tot.block<6,6>(i*6,i*6) += Hij.block<6,6>(0,0);
 
                        H_tot.block<6+9+9,6+9+9>(N*6,N*6) += Hij.block<6+9+9,6+9+9>(6,6);
                        H_tot.block<6,6+9+9>(i*6,N*6) += Hij.block<6,6+9+9>(0,6);
                        H_tot.block<6+9+9,6>(N*6,i*6) += Hij.block<6+9+9,6>(6,0);
                }
        }
 
#if 0
        // Also include additional cost terms to stabilize estimation:
        // -----------------------------------------------------------------
        // In the left->right pose increment: Add cost if we know that both cameras are almost parallel:
        const double cost_lr_angular = 1e10;
        H_tot.block<3,3>(N*6+3,N*6+3) += Eigen::Matrix<double,3,3>::Identity() * cost_lr_angular;
#endif
 
        // Ignore those derivatives of "fixed" (non-optimizable) variables in camera parameters
        // --------------------------------------------------------------------------------------
        const size_t N_Cs = var_indxs.size();  // [0-8]
        const size_t nUnknowns = N*6 + 6 + 2*N_Cs;
        const size_t nUnkPoses = N*6 + 6;
 
        minus_g.setZero(nUnknowns);
        H.setZero(nUnknowns,nUnknowns);
        // Copy unmodified all but the cam. params:
        minus_g.block(0,0, nUnkPoses,1 )  = minus_g_tot.block(0,0, nUnkPoses,1);
        H.block(0,0, nUnkPoses,nUnkPoses) = H_tot.block(0,0, nUnkPoses,nUnkPoses);
 
        // Selective copy cam. params parts:
        for (size_t i=0;i<N_Cs;i++)
        {
                minus_g[nUnkPoses+i]      = minus_g_tot[nUnkPoses +     var_indxs[i] ];
                minus_g[nUnkPoses+N_Cs+i] = minus_g_tot[nUnkPoses + 9 + var_indxs[i] ];
        }
 
        for (size_t k=0;k<nUnkPoses;k++)
        {
                for (size_t i=0;i<N_Cs;i++)
                {
                        H(nUnkPoses+i,k) = H(k,nUnkPoses+i) = H_tot(k,nUnkPoses+var_indxs[i]);
                        H(nUnkPoses+i+N_Cs,k) = H(k,nUnkPoses+i+N_Cs) = H_tot(k,nUnkPoses+9+var_indxs[i]);
                }
        }
 
        for (size_t i=0;i<N_Cs;i++)
        {
                for (size_t j=0;j<N_Cs;j++)
                {
                        H(nUnkPoses+i,nUnkPoses+j) = H_tot( nUnkPoses + var_indxs[i], nUnkPoses + var_indxs[j] );
                        H(nUnkPoses+N_Cs+i,nUnkPoses+N_Cs+j) = H_tot( nUnkPoses +9+var_indxs[i], nUnkPoses +9+ var_indxs[j] );
                }
        }
 
#if 0
        {
                CMatrixDouble M1 = H_tot;
                CMatrixDouble g1 = minus_g_tot;
                M1.saveToTextFile("H1.txt");
                g1.saveToTextFile("g1.txt");
 
                CMatrixDouble M2 = H;
                CMatrixDouble g2 = minus_g;
                M2.saveToTextFile("H2.txt");
                g2.saveToTextFile("g2.txt");
                mrpt::system::pause();
        }
#endif
 
} // end of build_linear_system
 
 
//  * N x Manifold Epsilon of left camera pose (6)
//  * Manifold Epsilon of right2left pose (6)
//  * Left-cam-params (<=9)
//  * Right-cam-params (<=9)
void mrpt::vision::add_lm_increment(
        const Eigen::VectorXd & eps,
        const vector_size_t   & var_indxs,
        lm_stat_t             & lm_stat)
{
        // Increment of the N cam poses
        const size_t N = lm_stat.valid_image_pair_indices.size();
        for (size_t i=0;i<N;i++)
        {
                CPose3D &cam_pose = lm_stat.left_cam_poses[ lm_stat.valid_image_pair_indices[i] ];
 
                // Use the Lie Algebra methods for the increment:
                const CArrayDouble<6> incr( &eps[6*i] );
                const CPose3D         incrPose = CPose3D::exp(incr);
 
                //new_pose =  old_pose  [+] delta
                //         = exp(delta) (+) old_pose
                cam_pose.composeFrom(incrPose, cam_pose );
        }
 
        // Increment of the right-left pose:
        {
                // Use the Lie Algebra methods for the increment:
                const CArrayDouble<6> incr( &eps[6*N] );
                const CPose3D         incrPose = CPose3D::exp(incr);
 
                //new_pose =  old_pose  [+] delta
                //         = exp(delta) (+) old_pose
                lm_stat.right2left_pose.composeFrom(incrPose, lm_stat.right2left_pose );
        }
 
        // Increment of the camera params:
        const size_t idx=6*N+6;
        const size_t nPC = var_indxs.size();  // Number of camera parameters being optimized
        for (size_t i=0;i<nPC;i++)
        {
                lm_stat.left_cam_params [var_indxs[i]] += eps[idx+i];
                lm_stat.right_cam_params[var_indxs[i]] += eps[idx+nPC+i];
        }
 
} // end of add_lm_increment
 
 
#ifdef USE_NUMERIC_JACOBIANS
// ---------------------------------------------------------------
// Aux. function, only if we evaluate Jacobians numerically:
// ---------------------------------------------------------------
        struct TNumJacobData
        {
                const lm_stat_t & lm_stat;
                const TPoint3D  & obj_point;
                const CPose3D   & left_cam;
                const CPose3D   & right2left;
                const Eigen::Matrix<double,4,1> & real_obs;
 
                TNumJacobData(
                        const lm_stat_t & _lm_stat,
                        const TPoint3D  & _obj_point,
                        const CPose3D   & _left_cam,
                        const CPose3D   & _right2left,
                        const Eigen::Matrix<double,4,1> &_real_obs
                        ) : lm_stat(_lm_stat), obj_point(_obj_point), left_cam(_left_cam), right2left(_right2left), real_obs(_real_obs)
                {
                }
 
        };
 
        void numeric_jacob_eval_function(
                const CArrayDouble<30> &x,
                const TNumJacobData &dat,
                CArrayDouble<4> &out)
        {
                // Recover the state out from "x":
                const CArrayDouble<6> incr_l( &x[0] );
                const CPose3D         incrPose_l = CPose3D::exp(incr_l);
                const CArrayDouble<6> incr_rl( &x[6] );
                const CPose3D         incrPose_rl = CPose3D::exp(incr_rl);
 
                // [fx fy cx cy k1 k2 k3 t1 t2]
                TStereoCamera cam_params;
                CArrayDouble<9> left_cam_params = x.segment<9>(6+6);
                cam_params.leftCamera.fx( left_cam_params[0] );
                cam_params.leftCamera.fy( left_cam_params[1] );
                cam_params.leftCamera.cx( left_cam_params[2] );
                cam_params.leftCamera.cy( left_cam_params[3] );
                cam_params.leftCamera.k1( left_cam_params[4] );
                cam_params.leftCamera.k2( left_cam_params[5] );
                cam_params.leftCamera.k3( left_cam_params[6] );
                cam_params.leftCamera.p1( left_cam_params[7] );
                cam_params.leftCamera.p2( left_cam_params[8] );
 
                // [fx fy cx cy k1 k2 k3 t1 t2]
                CArrayDouble<9> right_cam_params = x.segment<9>(6+6+9);
                cam_params.rightCamera.fx( right_cam_params[0] );
                cam_params.rightCamera.fy( right_cam_params[1] );
                cam_params.rightCamera.cx( right_cam_params[2] );
                cam_params.rightCamera.cy( right_cam_params[3] );
                cam_params.rightCamera.k1( right_cam_params[4] );
                cam_params.rightCamera.k2( right_cam_params[5] );
                cam_params.rightCamera.k3( right_cam_params[6] );
                cam_params.rightCamera.p1( right_cam_params[7] );
                cam_params.rightCamera.p2( right_cam_params[8] );
 
                TPixelCoordf px_l;
                project_point( dat.obj_point,cam_params.leftCamera, incrPose_l + dat.left_cam, px_l );
                TPixelCoordf px_r;
                project_point( dat.obj_point,cam_params.rightCamera, incrPose_rl + dat.right2left + incrPose_l + dat.left_cam, px_r );
 
                const Eigen::Matrix<double,4,1> predicted_obs( px_l.x,px_l.y, px_r.x,px_r.y );
 
                // Residual:
                out = predicted_obs; // - dat.real_obs;
        }
 
        void eval_h_b(
                const CArrayDouble<2> &X,
                const TCamera &params,
                CArrayDouble<2> &out)
        {
                // Radial distortion:
                const double x = X[0], y=X[1];
                const double r2 = square(x)+square(y);
                const double r4 = square(r2);
                const double r6 = r2*r4;
                const double A  = 1+params.dist[0]*r2+params.dist[1]*r4+params.dist[4]*r6;
                const double B  = 2*x*y;
 
                out[0] = params.cx() + params.fx() * ( x*A + params.dist[2]*B + params.dist[3]*(r2+2*square(x)) );
                out[1] = params.cy() + params.fy() * ( y*A + params.dist[3]*B + params.dist[2]*(r2+2*square(y)) );
        }
 
        void eval_b_p(
                const CArrayDouble<3> &P,
                const int &dummy,
                CArrayDouble<2> &b)
        {
                // Radial distortion:
                b[0] = P[0]/P[2];
                b[1] = P[1]/P[2];
        }
 
 
        void eval_deps_D_p(
                const CArrayDouble<6> &eps,
                const TPoint3D &D_p,
                CArrayDouble<3> &out)
        {
                const CArrayDouble<6> incr( &eps[0] );
                const CPose3D         incrPose = CPose3D::exp(incr);
                TPoint3D D_p_out;
                incrPose.composePoint(D_p,D_p_out);
                for (int i=0;i<3;i++) out[i]=D_p_out[i];
        }
 
        struct TEvalData_A_eps_D_p
        {
                CPose3D  A, D;
                TPoint3D p;
        };
 
        void eval_dA_eps_D_p(
                const CArrayDouble<6> &eps,
                const TEvalData_A_eps_D_p  &dat,
                CArrayDouble<3> &out)
        {
                const CArrayDouble<6> incr( &eps[0] );
                const CPose3D  incrPose = CPose3D::exp(incr);
                const CPose3D  A_eps_D = dat.A + (incrPose + dat.D);
                TPoint3D pt;
                A_eps_D.composePoint(dat.p,pt);
                for (int i=0;i<3;i++) out[i]=pt[i];
        }
// ---------------------------------------------------------------
// End of aux. function, only if we evaluate Jacobians numerically:
// ---------------------------------------------------------------
#endif
 
 
 
 
 
// the 4x1 prediction are the (u,v) pixel coordinates for the left / right cameras:
double mrpt::vision::recompute_errors_and_Jacobians(
        const lm_stat_t       & lm_stat,
        TResidualJacobianList & res_jac,
        bool use_robust_kernel,
        double kernel_param )
{
        double total_err=0;
        const size_t N = lm_stat.valid_image_pair_indices.size();
        res_jac.resize(N);
 
        //Parse lm_stat data: CArrayDouble<9> left_cam_params, right_cam_params; // [fx fy cx cy k1 k2 k3 t1 t2]
        TCamera camparam_l;
        camparam_l.fx( lm_stat.left_cam_params[0] ); camparam_l.fy( lm_stat.left_cam_params[1] );
        camparam_l.cx( lm_stat.left_cam_params[2] ); camparam_l.cy( lm_stat.left_cam_params[3] );
        camparam_l.k1( lm_stat.left_cam_params[4] ); camparam_l.k2( lm_stat.left_cam_params[5] ); camparam_l.k3( lm_stat.left_cam_params[6] );
        camparam_l.p1( lm_stat.left_cam_params[7] ); camparam_l.p2( lm_stat.left_cam_params[8] );
        TCamera camparam_r;
        camparam_r.fx( lm_stat.right_cam_params[0] ); camparam_r.fy( lm_stat.right_cam_params[1] );
        camparam_r.cx( lm_stat.right_cam_params[2] ); camparam_r.cy( lm_stat.right_cam_params[3] );
        camparam_r.k1( lm_stat.right_cam_params[4] ); camparam_r.k2( lm_stat.right_cam_params[5] ); camparam_r.k3( lm_stat.right_cam_params[6] );
        camparam_r.p1( lm_stat.right_cam_params[7] ); camparam_r.p2( lm_stat.right_cam_params[8] );
 
        // process all points from all N image pairs:
        for (size_t k=0;k<N;k++)
        {
                const size_t k_idx = lm_stat.valid_image_pair_indices[k];
                const size_t nPts = lm_stat.obj_points.size();
                res_jac[k].resize(nPts);
                // For each point in the pattern:
                for (size_t i=0;i<nPts;i++)
                {
                        // Observed corners:
                        const TPixelCoordf &px_obs_l = lm_stat.images[k_idx].left.detected_corners[i];
                        const TPixelCoordf &px_obs_r = lm_stat.images[k_idx].right.detected_corners[i];
                        const Eigen::Matrix<double,4,1> obs(px_obs_l.x,px_obs_l.y,px_obs_r.x,px_obs_r.y);
 
                        TResidJacobElement & rje = res_jac[k][i];
 
                        // Predict i'th corner on left & right cameras:
                        // ---------------------------------------------
                        TPixelCoordf px_l, px_r;
                        project_point( lm_stat.obj_points[i], camparam_l, lm_stat.left_cam_poses[k_idx], px_l );
                        project_point( lm_stat.obj_points[i], camparam_r, lm_stat.right2left_pose+lm_stat.left_cam_poses[k_idx], px_r );
                        rje.predicted_obs = Eigen::Matrix<double,4,1>( px_l.x,px_l.y, px_r.x,px_r.y );
 
                        // Residual:
                        rje.residual = rje.predicted_obs - obs;
 
                        // Accum. total squared error:
                        const double err_sqr_norm = rje.residual.squaredNorm();
                        if (use_robust_kernel)
                        {
                                RobustKernel<rkPseudoHuber>  rk;
                                rk.param_sq = kernel_param;
 
                                double kernel_1st_deriv,kernel_2nd_deriv;
                                const double scaled_err = rk.eval(err_sqr_norm, kernel_1st_deriv,kernel_2nd_deriv );
 
                                rje.residual *= kernel_1st_deriv;
                                total_err += scaled_err;
                        }
                        else    total_err += err_sqr_norm;
 
                        // ---------------------------------------------------------------------------------
                        // Jacobian: (4x30)
                        // See technical report cited in the headers for the theory behind these formulas.
                        // ---------------------------------------------------------------------------------
#if !defined(USE_NUMERIC_JACOBIANS) || defined(COMPARE_NUMERIC_JACOBIANS)
                        // ----- Theoretical Jacobians -----
                        Eigen::Matrix<double,2,6> dhl_del, dhr_del, dhr_der;
                        Eigen::Matrix<double,2,9> dhl_dcl, dhr_dcr;
 
                        // 3D coordinates of the corner point wrt both cameras:
                        TPoint3D pt_wrt_left, pt_wrt_right;
                        lm_stat.left_cam_poses[k_idx].composePoint(lm_stat.obj_points[i], pt_wrt_left );
                        (lm_stat.right2left_pose+lm_stat.left_cam_poses[k_idx]).composePoint(lm_stat.obj_points[i], pt_wrt_right);
 
                        // Build partial Jacobians:
                        Eigen::Matrix<double,2,2> dhl_dbl, dhr_dbr;
                        jacob_dh_db_and_dh_dc(pt_wrt_left,  lm_stat.left_cam_params,  dhl_dbl, dhl_dcl );
                        jacob_dh_db_and_dh_dc(pt_wrt_right, lm_stat.right_cam_params, dhr_dbr, dhr_dcr );
 
#if 0
                        // Almost exact....
                        {
                                CArrayDouble<2> x0;
                                TPoint3D nP = pt_wrt_left;
                                x0[0] = nP.x/nP.z;
                                x0[1] = nP.y/nP.z;
 
                                CArrayDouble<2> x_incrs;
                                x_incrs.setConstant(1e-6);
 
                                Eigen::Matrix<double,2,2> num_dhl_dbl, num_dhr_dbr;
                                mrpt::math::jacobians::jacob_numeric_estimate(x0, &eval_h_b, x_incrs, camparam_l, num_dhl_dbl );
 
                                nP = pt_wrt_right;
                                x0[0] = nP.x/nP.z;
                                x0[1] = nP.y/nP.z;
                                mrpt::math::jacobians::jacob_numeric_estimate(x0, &eval_h_b, x_incrs, camparam_r, num_dhr_dbr );
 
                                cout << "num_dhl_dbl:\n" << num_dhl_dbl << "\ndiff dhl_dbl:\n" << dhl_dbl-num_dhl_dbl << endl << endl;
                                cout << "num_dhr_dbr:\n" << num_dhr_dbr << "\ndiff dhr_dbr:\n" << dhr_dbr-num_dhr_dbr << endl << endl;
 
                        }
#endif
 
                        Eigen::Matrix<double,2,3> dbl_dpl, dbr_dpr;
                        jacob_db_dp(pt_wrt_left,  dbl_dpl);
                        jacob_db_dp(pt_wrt_right, dbr_dpr);
 
#if 0
                        // OK! 100% exact.
                        {
                                CArrayDouble<3> x0;
                                x0[0]=pt_wrt_left.x;
                                x0[1]=pt_wrt_left.y;
                                x0[2]=pt_wrt_left.z;
 
                                CArrayDouble<3> x_incrs;
                                x_incrs.setConstant(1e-8);
 
                                Eigen::Matrix<double,2,3> num_dbl_dpl, num_dbr_dpr;
                                const int dumm=0;
                                mrpt::math::jacobians::jacob_numeric_estimate(x0, &eval_b_p, x_incrs, dumm, num_dbl_dpl );
 
                                x0[0]=pt_wrt_right.x;
                                x0[1]=pt_wrt_right.y;
                                x0[2]=pt_wrt_right.z;
                                mrpt::math::jacobians::jacob_numeric_estimate(x0, &eval_b_p, x_incrs, dumm, num_dbr_dpr );
 
                                cout << "num_dbl_dpl:\n" << num_dbl_dpl << "\ndbl_dpl:\n" << dbl_dpl << endl << endl;
                                cout << "num_dbr_dpr:\n" << num_dbr_dpr << "\ndbr_dpr:\n" << dbr_dpr << endl << endl;
 
                        }
#endif
 
                        // p_l = exp(epsilon_l) (+) pose_left (+) point_ij
                        // p_l = [exp(epsilon_r) (+) pose_right2left] (+) [exp(epsilon_l) (+) pose_left] (+) point_ij
                        Eigen::Matrix<double,3,6> dpl_del, dpr_del, dpr_der;
                        jacob_deps_D_p_deps(pt_wrt_left,  dpl_del);
                        jacob_deps_D_p_deps(pt_wrt_right, dpr_der);
                        jacob_dA_eps_D_p_deps(lm_stat.right2left_pose, lm_stat.left_cam_poses[k_idx],lm_stat.obj_points[i], dpr_del);
 
#if 0
                        // 100% Exact.
                        {
                                // Test jacob_deps_D_p_deps:
                                CArrayDouble<6> x0;
                                x0.setConstant(0);
 
                                CArrayDouble<6> x_incrs;
                                x_incrs.setConstant(1e-8);
 
                                Eigen::Matrix<double,3,6> num_dpl_del, num_dpr_der;
                                mrpt::math::jacobians::jacob_numeric_estimate(x0, &eval_deps_D_p, x_incrs, pt_wrt_left , num_dpl_del );
                                mrpt::math::jacobians::jacob_numeric_estimate(x0, &eval_deps_D_p, x_incrs, pt_wrt_right, num_dpr_der );
 
                                cout << "num_dpl_del:\n" << num_dpl_del << "\ndiff dpl_del:\n" << dpl_del-num_dpl_del << endl << endl;
                                cout << "num_dpr_der:\n" << num_dpr_der << "\ndiff dpr_der:\n" << dpr_der-num_dpr_der << endl << endl;
                        }
#endif
 
#if 0
                        // 100% Exact.
                        {
                                // Test jacob_dA_eps_D_p_deps:
                                CArrayDouble<6> x0;
                                x0.setConstant(0);
 
                                CArrayDouble<6> x_incrs;
                                x_incrs.setConstant(1e-8);
 
                                TEvalData_A_eps_D_p dat;
                                dat.A = lm_stat.right2left_pose;
                                dat.D = lm_stat.left_cam_poses[k_idx];
                                dat.p = lm_stat.obj_points[i];
 
                                Eigen::Matrix<double,3,6> num_dpr_del;
                                mrpt::math::jacobians::jacob_numeric_estimate(x0, &eval_dA_eps_D_p, x_incrs,dat , num_dpr_del );
 
                                cout << "num_dpr_del:\n" << num_dpr_del << "\ndiff dpr_del:\n" << num_dpr_del-dpr_del << endl << endl;
                        }
#endif
 
                        // Jacobian chain rule:
                        dhl_del = dhl_dbl * dbl_dpl * dpl_del;
                        dhr_del = dhr_dbr * dbr_dpr * dpr_del;
                        dhr_der = dhr_dbr * dbr_dpr * dpr_der;
 
                        rje.J.setZero(4,30);
                        rje.J.block<2,6>(0,0) = dhl_del;
                        rje.J.block<2,6>(2,0) = dhr_del;
                        rje.J.block<2,6>(2,6) = dhr_der;
                        rje.J.block<2,9>(0,12) = dhl_dcl;
                        rje.J.block<2,9>(2,21) = dhr_dcr;
 
#       if defined(COMPARE_NUMERIC_JACOBIANS)
                        const Eigen::Matrix<double,4,30> J_theor=rje.J;
#       endif
                        // ---- end of theoretical Jacobians ----
#endif
 
#if defined(USE_NUMERIC_JACOBIANS) || defined(COMPARE_NUMERIC_JACOBIANS)
                        // ----- Numeric Jacobians ----
 
                        CArrayDouble<30> x0;  // eps_l (6) + eps_lr (6) + l_camparams (9) + r_camparams (9)
                        x0.setZero();
                        x0.segment<9>(6+6)   = lm_stat.left_cam_params;
                        x0.segment<9>(6+6+9) = lm_stat.right_cam_params;
 
                        const double x_incrs_val[30] =  {
                                1e-6,1e-6,1e-6, 1e-7,1e-7,1e-7,  // eps_l
                                1e-6,1e-6,1e-6, 1e-7,1e-7,1e-7,  // eps_r
                                1e-3,1e-3,1e-3,1e-3, 1e-8,1e-8,1e-8,1e-8, 1e-4,  // cam_l
                                1e-3,1e-3,1e-3,1e-3, 1e-8,1e-8,1e-8,1e-8, 1e-4  // cam_rl
                        };
                        const CArrayDouble<30> x_incrs(x_incrs_val);
                        TNumJacobData dat(lm_stat,lm_stat.obj_points[i],lm_stat.left_cam_poses[k_idx], lm_stat.right2left_pose, obs);
 
                        mrpt::math::jacobians::jacob_numeric_estimate(x0, &numeric_jacob_eval_function, x_incrs, dat, rje.J );
 
#       if defined(COMPARE_NUMERIC_JACOBIANS)
                        const Eigen::Matrix<double,4,30> J_num=rje.J;
#       endif
#endif          // ---- end of numeric Jacobians ----
 
// Only for debugging:
#if defined(COMPARE_NUMERIC_JACOBIANS)
                        //if ( (J_num-J_theor).array().abs().maxCoeff()>1e-2)
                        {
                                ofstream f;
                                f.open("dbg.txt", ios_base::out | ios_base::app);
                                f << "J_num:\n" << J_num << endl
                                  << "J_theor:\n" << J_theor << endl
                                  << "diff:\n" << J_num - J_theor << endl
                                  << "diff (ratio):\n" << (J_num - J_theor).cwiseQuotient(J_num) << endl << endl;
                        }
#endif
                } // for i
        } // for k
 
        return total_err;
} // end of recompute_errors_and_Jacobians
 
 
// Ctor:
TStereoCalibParams::TStereoCalibParams() :
        check_size_x(7),check_size_y(9),
        check_squares_length_X_meters(0.02),check_squares_length_Y_meters(0.02),
        normalize_image(true),
        skipDrawDetectedImgs(false),
        verbose(true),
        maxIters(2000),
        optimize_k1(true), optimize_k2(true), optimize_k3(false),
        optimize_t1(false),optimize_t2(false),
        use_robust_kernel(false),
        robust_kernel_param(10),
        callback(NULL),
        callback_user_param(NULL)
{
}
 
TStereoCalibResults::TStereoCalibResults() :
        final_rmse (0),
        final_iters(0),
        final_number_good_image_pairs(0)
{
}