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bundle_adjuster.h
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/**
* @author Hauke Strasdat
*
* Copyright (C) 2010 Hauke Strasdat
* Imperial College London
*
* bundle_adjuster.h is part of RobotVision.
*
* RobotVision is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation, either version 3 of the
* License, or any later version.
*
* RobotVision is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* and the GNU Lesser General Public License along with this program.
* If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef RV_BUNDLE_ADJUSTER_H
#define RV_BUNDLE_ADJUSTER_H
#include <vector>
#include <map>
#include <TooN/TooN.h>
#include <TooN/TooN.h>
#include <TooN/se3.h>
#include <TooN/se2.h>
#include <TooN/Cholesky.h>
#include <cvd/image_io.h>
#include "maths_utils.h"
#include "sparse_matrix.h"
#include "sparse_cholesky.h"
#include "transformations.h"
#include "stopwatch.h"
#ifdef RV_BUILD_RELEASE
//std::vector bounds check only for debug build
#define AT(x,y) x[y]
//MARCRO definitions in headers are in general not a good idea,
//but here its is undefined later, so it is ok.
#else
#define AT(x,y) x.at(y)
//MARCRO definitions in headers are in general not a good idea,
//but here its is undefined later, so it is ok.
#endif
namespace RobotVision
{
/**
* Parameter class for BundleAdjuster (see below)
*/
class BundleAdjusterParams
{
public:
BundleAdjusterParams(bool robust_kernel=true,
double kernel_param=1,
int num_iter=50,
double initial_mu=-1,
double final_mu_factor=999999.,
double tau = 0.00001):
robust_kernel(robust_kernel),
kernel_param(kernel_param),
num_iter(num_iter),
initial_mu(initial_mu),
final_mu_factor(final_mu_factor),
tau(tau)
{
}
bool robust_kernel;
double kernel_param;
int num_iter;
double initial_mu;
double final_mu_factor;
double tau;
};
/**
* Helper class for BundleAdjuster (see below)
*/
template <int FrameDoF, int PointDoF, int ObsDim> class JacData
{
public:
JacData()
{
double nan = NAN;
J_frame = TooN::Ones(ObsDim,FrameDoF)*nan;
J_point = TooN::Ones(ObsDim,PointDoF)*nan;
J_anchor = TooN::Ones(ObsDim,FrameDoF)*nan;
}
public:
TooN::Matrix<ObsDim,FrameDoF> J_frame;
TooN::Matrix<ObsDim,PointDoF> J_point;
TooN::Matrix<ObsDim,FrameDoF> J_anchor;
};
/**
*
* This class performs Bundle adjustment (BA) using Levenberg-Marquardt (LM)
* as described in
*
* > H. Strasdat, J.M.M. Montiel, A.J. Davison:
* "Scale Drift-Aware Large Scale Monocular SLAM",
* Proc. of Robotics: Science and Systems (RSS),
* Zaragoza, Spain, 2010.
* http://www.roboticsproceedings.org/rss06/p10.html <
*
* The the update strategy of mu follows
* >M.I. A. Lourakis and A.A. Argyros, "The Design and Implementation
* of a Generic Sparse Bundle Adjustment Software Package Based on
* the Levenberg-Marquardt Algorithm", Technical Report, 2004.<
* and the implementation of the Schur complement is inspired by
* >C. Engels, H. Stewnius, D. Nister, "Bundle Adjustment Rules",
* Photogrammetric Computer Vision (PCV), September 2006.
* and
* >K. Konolige, "Sparse Sparse Bundle Adjustment", BMVC 2010.
*
* Frame: How is the frame/pose represented? (e.g. SE3)
* FrameDoF: How many DoF has the pose/frame? (e.g. 6 DoF, that is
* 3 DoF translation, 3 DoF rotation)
* PointParNum: number of parameters to represent a point
* (4 for a 3D homogenious point)
* PointDoF: DoF of a point (3 DoF for a 3D homogenious point)
* ObsDim: dimensions of observation (2 dim for (u,res) image
* measurement)
*/
template <typename Frame,
int FrameDoF,
int PointParNum,
int PointDoF,
typename Obs,
int ObsDim>
class BundleAdjuster
{
public:
typedef AbstractPrediction<Frame,FrameDoF,PointParNum,PointDoF,ObsDim>
_AbsJac;
typedef std::vector<JacData<FrameDoF, PointDoF,ObsDim> >
_JacVec;
typedef std::vector<TooN::Vector<PointParNum> >
_PointVec;
typedef std::list<Obs>
_Track;
typedef std::map<int,_Track >
_TrackMap;
BundleAdjuster(){
verbose = 0;
}
int verbose;
inline void SchurComp(const _Track & track,
int num_fix_frames,
int point_id,
const std::vector<TooN::Matrix<PointDoF,FrameDoF> > & H_p,
const TooN::Matrix<PointDoF,PointDoF> & inv_H_pp,
const TooN::Vector<PointDoF> & t_p,
std::vector<std::map<int,TooN::Matrix<FrameDoF,PointDoF> > > & T_,
TooN::Vector<> & B,
RowBlockMapVec<FrameDoF> & A_)
{
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs = *it_cam;
int frame_id = id_obs.frame_id-num_fix_frames;
if (frame_id>=0)
{
TooN::Matrix<FrameDoF,PointDoF> T_pc
= AT(H_p,frame_id).T()*inv_H_pp;
AT(T_,point_id).insert(std::make_pair(frame_id, T_pc));
B.slice(frame_id*FrameDoF,FrameDoF)
-= AT(H_p,frame_id).T()*t_p;
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs2 = *it_cam;
int frame_id2 = id_obs2.frame_id-num_fix_frames;
if (frame_id2>=frame_id)
{
TooN::Matrix<FrameDoF,FrameDoF> tmp
= -(T_pc*AT(H_p,frame_id2));
A_.add(tmp,frame_id,frame_id2);
if (frame_id!= frame_id2)
{
A_.add(tmp.T(),frame_id2,frame_id);
}
}
}
}
}
}
inline void SchurCompUpperTriag
(const _Track & track,
int num_fix_frames,
int point_id,
const std::vector<TooN::Matrix<PointDoF,FrameDoF> > & H_p,
const TooN::Matrix<PointDoF,PointDoF> & inv_H_pp,
const TooN::Vector<PointDoF> & t_p,
std::vector<std::map<int,TooN::Matrix<FrameDoF,PointDoF> > > & T_,
TooN::Vector<> & B,
RowBlockMapVec<FrameDoF> & A_)
{
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs = *it_cam;
int frame_id = id_obs.frame_id-num_fix_frames;
if (frame_id>=0)
{
TooN::Matrix<FrameDoF,PointDoF> T_pc
= AT(H_p,frame_id).T()*inv_H_pp;
AT(T_,point_id).insert(std::make_pair(frame_id, T_pc));
B.slice(frame_id*FrameDoF,FrameDoF)
-= AT(H_p,frame_id).T()*t_p;
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs2 = *it_cam;
int frame_id2 = id_obs2.frame_id-num_fix_frames;
if (frame_id2>=frame_id)
{
TooN::Matrix<FrameDoF,FrameDoF> tmp
= -(T_pc*AT(H_p,frame_id2));
A_.add(tmp,frame_id,frame_id2);
}
}
}
}
}
double calcFast(std::vector<Frame > & frame_list,
_PointVec & point_list,
_AbsJac & prediction,
const _TrackMap & track_list,
const int num_fix_frames,
const BundleAdjusterParams & ba_params,
int embedded_point_iter = 0,
bool use_qr= false)
{
assert(track_list.size()>0);
assert(point_list.size()==track_list.size());
#ifndef RV_SUITESPARSE_SUPPORT
use_qr = false; //QR is not implemented using CSparse (yet)
#endif
int num_points = point_list.size();
int num_frames = frame_list.size();
double chi2 = 0;
std::vector<Frame > new_frame_list(frame_list);
_PointVec new_point_list(point_list);
double norm_max_A = 0.;
for (typename _TrackMap::const_iterator it_tl = track_list.begin();
it_tl != track_list.end();
++it_tl)
{
int point_id = it_tl->first;
const _Track & track = it_tl->second;
TooN::Vector<PointParNum> & point = AT(point_list,point_id);
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs = *it_cam;
Frame & frame = AT(frame_list,id_obs.frame_id);
TooN::Matrix<ObsDim,PointDoF> J_p;
TooN::Matrix<ObsDim,FrameDoF> J_c;
TooN::Vector<ObsDim> f = id_obs.obs
- prediction.map_n_bothJac(frame,
point,
J_c,
J_p);
norm_max_A
= max(norm_max_A,
max(norm_max(mulW(J_c,J_c,id_obs).diagonal_slice()),
norm_max(mulW(J_p,J_p,id_obs).diagonal_slice())));
chi2 += sqrW(f,id_obs);
}
}
double mu = ba_params.initial_mu;
if (ba_params.initial_mu==-1)
{
mu = ba_params.tau*norm_max_A;
}
double final_mu = ba_params.final_mu_factor*mu;
if (verbose>0)
{
std::cout << "chi2: "<< chi2 << std::endl;
}
double nu = 2;
double eps =0.000000000000001;
bool stop = false;
for (int i_g=0; i_g<ba_params.num_iter; i_g++)
{
if (verbose>0)
{
std::cout << "iteration: "<< i_g << std::endl;
}
double rho = 0; //Assign value, so the compiler is not complaining...
do{
if (verbose>0)
{
std::cout << "mu: " <<mu<< std::endl;
}
TooN::Matrix<FrameDoF,FrameDoF> muI_f = mu*TooN::Identity;
TooN::Matrix<PointDoF,PointDoF> muI_p = mu*TooN::Identity;
RowBlockMapVec<FrameDoF> A_(num_frames-num_fix_frames);
TooN::Vector<> B = TooN::Zeros(FrameDoF*(num_frames-num_fix_frames));
std::vector<TooN::Matrix<PointDoF,FrameDoF> >
H_p(num_frames-num_fix_frames);
std::vector<TooN::Vector<PointDoF> > t_(num_points);
std::vector<std::map<int,TooN::Matrix<FrameDoF,PointDoF> > >
T_(num_points);
for (typename _TrackMap::const_iterator it_tl = track_list.begin();
it_tl != track_list.end();
++it_tl)
{
int point_id = it_tl->first;
const _Track & track = it_tl->second;
TooN::Vector<PointParNum> & point = AT(point_list,point_id);
TooN::Matrix<PointDoF,PointDoF> H_pp = muI_p;
TooN::Vector<PointDoF> b_p = TooN::Zeros;
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs = *it_cam;
Frame & frame = AT(frame_list,id_obs.frame_id);
int frame_id = id_obs.frame_id-num_fix_frames;
assert(id_obs.point_id==point_id);
TooN::Matrix<ObsDim,PointDoF> J_p;
TooN::Matrix<ObsDim,FrameDoF> J_c;
TooN::Vector<ObsDim> f = id_obs.obs
- prediction.map_n_bothJac(frame,
point,
J_c,
J_p);
J_c *= -1;
J_p *= -1;
if(ba_params.robust_kernel)
{
double nrm = std::max(0.00000000001,
sqrt(sqrW(f,id_obs)));
double w = sqrt(kernel(nrm,ba_params.kernel_param))/nrm;
f *= w;
}
H_pp += mulW(J_p,J_p,id_obs);
b_p -= mulW(J_p,f,id_obs);
if (frame_id>=0)
{
A_.add(mulW(J_c,J_c,id_obs), muI_f, frame_id, frame_id);
AT(H_p,frame_id) = mulW(J_p,J_c,id_obs);
B.slice(frame_id*FrameDoF,FrameDoF) -= mulW(J_c,f,id_obs);
}
}
TooN::Cholesky<PointDoF> Ch_H_pp(H_pp);
TooN::Matrix<PointDoF,PointDoF> inv_H_pp = Ch_H_pp.get_inverse();
TooN::Vector<PointDoF> t_p = inv_H_pp*b_p;
AT(t_,point_id) = t_p;
if (use_qr)
{
SchurComp(track,
num_fix_frames,
point_id,
H_p,
inv_H_pp,
t_p,
T_,
B,
A_);
}
else
{
SchurCompUpperTriag(track,
num_fix_frames,
point_id,
H_p,
inv_H_pp,
t_p,
T_,
B,
A_);
}
}
int matrix_type = 1;//symmetric, upper_triangle
if (use_qr)
{
matrix_type = 0;//unsymmetric
}
SparseMatrix<> sS(A_,matrix_type);
TooN::Vector<> delta((num_frames-num_fix_frames)*FrameDoF);
try{
SparseSolver<> Ch(sS);
delta = Ch.backsub(B);
}catch (NotPosSemiDefException & e) {
// not positive definite so increase mu and try again
std::cout << "Not pose Def" << std::endl;
mu *= nu;
nu *= 2.;
stop = (mu>final_mu);
continue;
}
for (int i=0; i<num_frames-num_fix_frames; ++i)
{
AT(new_frame_list,i+num_fix_frames)
= prediction.add(AT(frame_list,i+num_fix_frames),
delta.slice(i*FrameDoF,FrameDoF));
}
for (typename _TrackMap::const_iterator it_tl = track_list.begin();
it_tl != track_list.end();
++it_tl)
{
int point_id = it_tl->first;
const _Track & track = it_tl->second;
TooN::Vector<PointDoF> dp = AT(t_,point_id);
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs = *it_cam;
int frame_id = id_obs.frame_id-num_fix_frames;
if (frame_id>=0)
{
dp -= AT(T_,point_id).find(frame_id)->second.T()
*delta.slice(frame_id*FrameDoF,FrameDoF);
}
}
AT(new_point_list,point_id)
= prediction.add(AT(point_list,point_id), dp);
}
double new_chi2 = 0;
if (embedded_point_iter>0)
{
new_chi2 = calcFastStructureOnly(new_frame_list,
new_point_list,
prediction,
track_list,
BundleAdjusterParams
(ba_params.robust_kernel,
ba_params.kernel_param,
embedded_point_iter,
ba_params.initial_mu,
ba_params.final_mu_factor));
}
else
{
for (typename _TrackMap::const_iterator it_tl = track_list.begin();
it_tl != track_list.end();
++it_tl)
{
int point_id = it_tl->first;
const _Track & track = it_tl->second;
TooN::Vector<PointParNum> & point = AT(new_point_list,point_id);
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs = *it_cam;
Frame & frame = AT(new_frame_list,id_obs.frame_id);
TooN::Vector<ObsDim> f = id_obs.obs - prediction.map(frame,
point);
new_chi2 += sqrW(f,id_obs);
}
}
}
if (isnan(new_chi2))
{
std::cerr << "res_new is NAN\n";
exit(-1);
}
if (verbose>0)
{
std::cout << "new chi2: "<< new_chi2 << std::endl;
}
rho = (chi2-new_chi2)/(delta*(mu*delta+B));
if(rho>0)
{
//std::cerr << mu << std::endl;
frame_list = std::vector<Frame> (new_frame_list);
point_list = _PointVec(new_point_list);
chi2 = new_chi2;
stop = norm_max(B)<=eps;
mu *= std::max(1./3.,1-Po3(2*rho-1));
nu = 2.;
}
else
{
if (verbose>0)
std::cout << "no update: chi vs. new_chi2 "
<< chi2
<< " vs. "
<< new_chi2
<< std::endl;
mu *= nu;
nu *= 2.;
stop = (mu>final_mu);
}
}while(!(rho>0 || stop));
if (stop)
break;
}
return chi2;
}
double calcFastStructureOnly(std::vector<Frame> & frame_list,
_PointVec & point_list,
_AbsJac & prediction,
const _TrackMap & track_list,
const BundleAdjusterParams & ba_params)
{
assert(point_list.size()==track_list.size());
assert(track_list.size()>0);
//int num_points = point_list.size();
double chi2_sum=0;
double old_chi2_sum=0;
for (typename _TrackMap
::const_iterator it_tl = track_list.begin();
it_tl != track_list.end();
++it_tl)
{
double nu = 2;
double eps =0.000000000000001;
bool stop = false;
int point_id = it_tl->first;
TooN::Vector<PointParNum> & point = AT(point_list,point_id);
const _Track & track = it_tl->second;
double chi2 = 0;
TooN::Vector<PointParNum> new_point = point;
double norm_max_A = 0.;
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs = *it_cam;
Frame & frame = AT(frame_list,id_obs.frame_id);
TooN::Matrix<ObsDim,PointDoF> J_p;
TooN::Vector<ObsDim> f = id_obs.obs
- prediction.map_n_pointJac(frame,
point,
J_p);
norm_max_A
= max(norm_max_A,
norm_max(mulW(J_p,J_p,id_obs).diagonal_slice()));
chi2 += sqrW(f,id_obs);
}
old_chi2_sum += chi2;
double mu = ba_params.initial_mu;
if (ba_params.initial_mu==-1)
{
mu = ba_params.tau*norm_max_A;
}
double final_mu = ba_params.final_mu_factor*mu;
for (int i_g=0; i_g<ba_params.num_iter; i_g++)
{
if (verbose>1)
{
std::cout << "iteration: "<< i_g << std::endl;
}
double rho = 0; //Assign value, so the compiler is not complaining...
do{
if (verbose>1)
{
std::cout << "mu: " <<mu<< std::endl;
}
TooN::Matrix<PointDoF,PointDoF> muI_p = mu*TooN::Identity;
TooN::Matrix<PointDoF,PointDoF> H_pp = muI_p;
TooN::Vector<PointDoF> b_p = TooN::Zeros;
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs = *it_cam;
Frame & frame = AT(frame_list,id_obs.frame_id);
TooN::Matrix<ObsDim,PointDoF> J_p;
TooN::Vector<ObsDim> f = id_obs.obs
- prediction.map_n_pointJac(frame,
point,
J_p);
J_p *= -1;
if(ba_params.robust_kernel)
{
double nrm = std::max(0.00000000001,
sqrt(sqrW(f,id_obs)));
double w = sqrt(kernel(nrm,ba_params.kernel_param))/nrm;
f *= w;
}
H_pp += mulW(J_p,J_p,id_obs);
b_p -= mulW(J_p,f,id_obs);
}
TooN::Vector<PointDoF> delta_p;
TooN::Cholesky<PointDoF> Ch_H_pp(H_pp);
delta_p = Ch_H_pp.backsub(b_p);
new_point
= prediction.add(point, delta_p);
double new_chi2 = 0;
for (typename _Track::const_iterator it_cam = track.begin();
it_cam!=track.end();
++it_cam)
{
const Obs & id_obs = *it_cam;
Frame & frame = AT(frame_list,id_obs.frame_id);
TooN::Vector<ObsDim> f = id_obs.obs - prediction.map(frame,
new_point);
new_chi2 += sqrW(f,id_obs);
}
if (isnan(new_chi2))
{
std::cerr << "res_new is NAN\n";
exit(-1);
}
rho = (chi2-new_chi2);//(delta*(mu*delta+g));
if(rho>0)
{
point = new_point;
chi2 = new_chi2;
stop = norm_max(b_p)<=eps;
mu *= std::max(1./3.,1-Po3(2*rho-1));
nu = 2.;
}
else
{
if (verbose>1)
std::cout << "no update: chi vs. new_chi2 "
<< chi2
<< " vs. "
<< new_chi2
<< std::endl;
mu *= nu;
nu *= 2.;
stop = (mu>final_mu);
}
}while(!(rho>0 || stop));
if (stop)
break;
}
chi2_sum += chi2;
}
if (verbose>0)
std::cout << " chi vs. new_chi2 "
<< old_chi2_sum
<< " vs. "
<< chi2_sum
<< std::endl;
return chi2_sum;
}
double calcFastMotionOnly(Frame & frame,
_PointVec & point_list,
_AbsJac & prediction,
const std::list<Obs> & obs_list,
const BundleAdjusterParams & ba_params)
{
// assert(point_list.size()==obs_list.size());
assert(obs_list.size()>0);
double nu = 2;
double eps =0.000000000000001;
bool stop = false;
double chi2 = 0;
Frame new_frame = frame;
double norm_max_A = 0.;
for (typename std::list<Obs>
::const_iterator it_cam = obs_list.begin();
it_cam != obs_list.end();
++it_cam)
{
int point_id = it_cam->point_id;
TooN::Vector<PointParNum> & point = AT(point_list,point_id);
const Obs & id_obs = *it_cam;
TooN::Matrix<ObsDim,FrameDoF> J_c;
TooN::Vector<ObsDim> f = id_obs.obs
- prediction.map_n_frameJac(frame,
point,
J_c);
norm_max_A = max(norm_max_A,
norm_max(mulW(J_c,J_c,id_obs).diagonal_slice()));
chi2 += sqrW(f,id_obs);
}
double mu = ba_params.initial_mu;
if (verbose>0)
{
std::cout << "init. chi2: "<< chi2 << std::endl;
}
if (ba_params.initial_mu==-1)
{
mu = ba_params.tau*norm_max_A;
}
double final_mu = ba_params.final_mu_factor*mu;
for (int i_g=0; i_g<ba_params.num_iter; i_g++)
{
if (verbose>0)
{
std::cout << "iteration: "<< i_g << std::endl;
}
double rho = 0; //Assign value, so the compiler is not complaining...
do{
if (verbose>0)
{
std::cout << "mu: " <<mu<< std::endl;
}
TooN::Vector<FrameDoF> B = TooN::Zeros;
TooN::Matrix<FrameDoF,FrameDoF> A = mu*TooN::Identity;
for (typename std::list<Obs>
::const_iterator it_cam = obs_list.begin();
it_cam != obs_list.end();
++it_cam)
{
int point_id = it_cam->point_id;
TooN::Vector<PointParNum> & point = AT(point_list,point_id);
const Obs & id_obs = *it_cam;
TooN::Matrix<ObsDim,FrameDoF> J_c;
TooN::Vector<ObsDim> f = id_obs.obs
- prediction.map_n_frameJac(frame,
point,
J_c);
J_c *= -1;
if(ba_params.robust_kernel)
{
double nrm = std::max(0.00000000001,
sqrt(sqrW(f,id_obs)));
double w = sqrt(kernel(nrm,ba_params.kernel_param))/nrm;
f *= w;
}
A += mulW(J_c,J_c,id_obs);
B.slice(0,FrameDoF) -= mulW(J_c,f,id_obs);
}
TooN::Vector<FrameDoF> delta;
TooN::Cholesky<FrameDoF> Ch(A);
delta = Ch.backsub(B);
// std::cout<< A << std::endl<< std::endl;
// std::cout<< B << std::endl<< std::endl;
//std::cout<< delta << std::endl<< std::endl;
new_frame
= prediction.add(frame,delta);
double new_chi2 = 0;
for (typename std::list<Obs>
::const_iterator it_cam = obs_list.begin();
it_cam != obs_list.end();
++it_cam)
{
int point_id = it_cam->point_id;
TooN::Vector<PointParNum> & point = AT(point_list,point_id);
const Obs & id_obs = *it_cam;
TooN::Vector<ObsDim> f = id_obs.obs - prediction.map(new_frame,
point);
new_chi2 += sqrW(f,id_obs);
}
if (isnan(new_chi2))
{
std::cerr << "res_new is NAN\n";
exit(-1);
}
rho = (chi2-new_chi2)/(delta*(mu*delta+B));