Slam.cpp
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00001 
00029 #include <iomanip>
00030 #include <vector>
00031 #include <map>
00032 #include <list>
00033 
00034 #include "isam/util.h"
00035 #include "isam/SparseSystem.h"
00036 #include "isam/OptimizationInterface.h"
00037 #include "isam/covariance.h"
00038 
00039 #include "isam/Slam.h"
00040 
00041 using namespace std;
00042 using namespace Eigen;
00043 
00044 namespace isam {
00045 
00046 // for numbering of factors and nodes
00047 int Node::_next_id = 0;
00048 int Factor::_next_id = 0;
00049 
00050 struct DeleteOnReturn
00051 {
00052   SparseVector** _ptr;
00053   explicit DeleteOnReturn(SparseVector** ptr) : _ptr(ptr) {}
00054   ~DeleteOnReturn() { delete [] _ptr; }
00055 };
00056 
00057 // for getting correct starting positions in matrix for each node,
00058 // only needed after removing nodes
00059 void Slam::update_starts() {
00060   int start = 0;
00061   const list<Node*>& nodes = get_nodes();
00062   for (list<Node*>::const_iterator it = nodes.begin(); it!=nodes.end(); it++) {
00063     Node* node = *it;
00064     node->_start = start;
00065     start += node->dim();
00066   }
00067 }
00068 
00069 Slam::Slam()
00070   : Graph(),
00071     _step(0), _prop(Properties()),
00072     _covariances(this),
00073     _require_batch(true), _cost_func(NULL),
00074     _dim_nodes(0), _dim_measure(0),
00075     _num_new_measurements(0), _num_new_rows(0),
00076     _opt(*this)
00077 {
00078 }
00079 
00080 Slam::~Slam()
00081 {
00082 }
00083 
00084 void Slam::save(const string fname) const {
00085   ofstream out(fname.c_str(), ios::out | ios::binary);
00086   require(out, "Slam.save: Cannot open output file.");
00087   write(out);
00088   out.close();
00089 }
00090 
00091 void Slam::add_node(Node* node) {
00092   Graph::add_node(node);
00093   _dim_nodes += node->dim();
00094 }
00095 
00096 void Slam::add_factor(Factor* factor) {
00097   // adds itself to factor lists of adjacent nodes; also initialized linked nodes if necessary
00098   factor->initialize_internal();
00099   // needed to change cost function
00100   factor->set_cost_function(&_cost_func);
00101   Graph::add_factor(factor);
00102   _num_new_measurements++;
00103   _num_new_rows += factor->dim();
00104   _dim_measure += factor->dim();
00105 }
00106 
00107 void Slam::remove_node(Node* node) {
00108   // make a copy, as the original will indirectly be modified below in remove_factor()
00109   list<Factor*> factors = node->factors(); 
00110   for (list<Factor*>::iterator factor = factors.begin(); factor!=factors.end(); factor++) {
00111     remove_factor(*factor);
00112   }
00113   _dim_nodes -= node->dim();
00114   Graph::remove_node(node);
00115   _require_batch = true;
00116 }
00117 
00118 void Slam::remove_factor(Factor* factor) {
00119   vector<Node*> nodes = factor->nodes();
00120   for (vector<Node*>::iterator node = nodes.begin(); node!=nodes.end(); node++) {
00121     (*node)->remove_factor(factor);
00122   }
00123   _dim_measure -= factor->dim();
00124   Graph::remove_factor(factor);
00125   _require_batch = true;
00126 }
00127 
00128 void Slam::incremental_update()
00129 {
00130   // incremental update not possible after removing nodes or factors
00131   // (might change in the future)
00132   if (_require_batch)
00133   {
00134     batch_optimization_step();
00135   }
00136   else if (_num_new_measurements > 0)
00137   {
00138     SparseSystem jac_new = jacobian_partial(_num_new_measurements);
00139 
00140     _opt.augment_sparse_linear_system(jac_new, _prop);
00141 
00142     _num_new_measurements = 0;
00143     _num_new_rows = 0;
00144   }
00145 }
00146 
00147 void Slam::batch_optimization_step()
00148 {
00149   _require_batch = false;
00150   // update linearization point x0 with current estimate x
00151   _num_new_measurements = 0;
00152   _num_new_rows = 0;
00153 
00154   _opt.relinearize(_prop);
00155 }
00156 
00157 UpdateStats Slam::update()
00158 {
00159   UpdateStats stats;
00160   stats.batch = false;
00161   stats.solve = false;
00162   if (_step%_prop.mod_update == 0)
00163   {
00164     if (_step%_prop.mod_batch == 0)
00165     {
00166       // batch solve periodically to avoid fill-in
00167       if (!_prop.quiet)
00168       {
00169         cout << endl;
00170         cout << "step " << _step;
00171       }
00172       batch_optimization_step();
00173       stats.batch = true;
00174     }
00175     else
00176     {
00177       // for efficiency, incrementally update most of the time.
00178       if (!_prop.quiet)
00179       {
00180         cout << ".";
00181         fflush(stdout);
00182       }
00183       incremental_update();
00184       if (_step%_prop.mod_solve == 0)
00185       {
00186         stats.solve = true;
00187 
00188         _opt.update_estimate(_prop);
00189       }
00190     }
00191   }
00192   _step++;
00193   stats.step = _step;
00194 
00195   return stats;
00196 }
00197 
00198 int Slam::batch_optimization()
00199 {
00200   int num_iterations = 0;
00201 
00202   int variables_deleted;
00203   int measurements_deleted;
00204   erase_marked(variables_deleted, measurements_deleted);
00205   _dim_nodes -= variables_deleted;
00206   _dim_measure -= measurements_deleted;
00207 
00208   _opt.batch_optimize(_prop, &num_iterations);
00209   return num_iterations;
00210 }
00211 
00212 void Slam::set_cost_function(cost_func_t func) {
00213   _cost_func = func;
00214 }
00215 
00216 void Slam::apply_exmap(const Eigen::VectorXd& x) {
00217   int pos = 0;
00218   for (list<Node*>::iterator node = _nodes.begin(); node != _nodes.end(); node++) {
00219     int dim = (*node)->dim();
00220     const VectorXd& xi = x.segment(pos, dim);
00221     (*node)->apply_exmap(xi);
00222     pos += dim;
00223   }
00224 }
00225 
00226 void Slam::self_exmap(const Eigen::VectorXd& x) {
00227   int pos = 0;
00228   for (list<Node*>::iterator node = _nodes.begin(); node != _nodes.end(); node++) {
00229     int dim = (*node)->dim();
00230     VectorXd xi = x.segment(pos, dim);
00231     (*node)->self_exmap(xi);
00232     pos += dim;
00233   }
00234 }
00235 
00236 void Slam::linpoint_to_estimate() {
00237   for (list<Node*>::iterator node = _nodes.begin(); node!=_nodes.end(); node++) {
00238     (*node)->linpoint_to_estimate();
00239   }
00240 }
00241 
00242 void Slam::estimate_to_linpoint() {
00243   for (list<Node*>::iterator node = _nodes.begin(); node!=_nodes.end(); node++) {
00244     (*node)->estimate_to_linpoint();
00245   }
00246 }
00247 
00248 void Slam::swap_estimates() {
00249   for (list<Node*>::iterator node = _nodes.begin(); node!=_nodes.end(); node++) {
00250     (*node)->swap_estimates();
00251   }
00252 }
00253 
00254 VectorXd Slam::weighted_errors(Selector s) {
00255   VectorXd werrors(_dim_measure);
00256   const list<Factor*>& factors = get_factors();
00257   int start = 0;
00258   for (list<Factor*>::const_iterator it = factors.begin(); it!=factors.end(); it++) {
00259     int dim = (*it)->dim();
00260     werrors.segment(start, dim) = (*it)->error(s);
00261     start += dim;
00262   }
00263   return werrors;
00264 }
00265 
00266 double Slam::chi2(Selector s) {
00267   return weighted_errors(s).squaredNorm();
00268 }
00269 
00270 double Slam::local_chi2(int last_n) {
00271   // avoiding two passes by allocating maximum size vector
00272   VectorXd werrors(_dim_measure);
00273   const list<Factor*>& factors = get_factors();
00274   int start = _dim_measure;
00275   int n = 0;
00276   for (list<Factor*>::const_reverse_iterator it = factors.rbegin();
00277       it!=factors.rend() && n<last_n;
00278       it++, n++) {
00279     int dim = (*it)->dim();
00280     start -= dim;
00281     werrors.segment(start, dim) = (*it)->error(ESTIMATE);
00282   }
00283   // only use actually calculated part of werrors
00284   return werrors.tail(_dim_measure-start).squaredNorm();
00285 }
00286 
00287 double Slam::normalized_chi2() {
00288   return chi2() / (double)(_dim_measure - _dim_nodes);
00289 }
00290 
00291 const SparseSystem& Slam::get_R() const {
00292   return _R;
00293 }
00294 
00295 const double epsilon = 0.0001;
00296 
00297 SparseSystem Slam::jacobian_numerical_columnwise() {
00298   // label starting points of rows, and initialize sparse row vectors with
00299   // correct number of entries
00300   int num_rows = _dim_measure;
00301   DeleteOnReturn rows_ptr(new SparseVector* [num_rows]);
00302   SparseVector** rows = rows_ptr._ptr; //[num_rows];
00303   int pos = 0;
00304   vector<int> factor_offset(get_factors().size());
00305   for (list<Factor*>::const_iterator it = get_factors().begin();
00306       it!=get_factors().end();
00307       it++) {
00308     (*it)->_start = pos;
00309     int dimtotal = 0;
00310     for (vector<Node*>::const_iterator it2 = (*it)->nodes().begin();
00311         it2!=(*it)->nodes().end();
00312         it2++) {
00313       dimtotal += (*it2)->dim();
00314     }
00315     for (int i=0; i<(*it)->dim(); i++, pos++) {
00316       // do not delete, will be pulled into SparseSystem below
00317       rows[pos] = new SparseVector(dimtotal);
00318     }
00319   }
00320   // larger than needed, but avoids some book keeping and avoids many (smaller) allocations
00321   VectorXd y_plus(num_rows);
00322   VectorXd y_minus(num_rows);
00323   int col = 0;
00324   for (list<Node*>::const_iterator it = get_nodes().begin(); it!=get_nodes().end(); it++) {
00325     Node* node = *it;
00326     int dim_node = node->dim();
00327     VectorXd delta(dim_node);
00328     delta.setZero();
00329     VectorXd original = node->vector0();
00330     for (int c=0; c<dim_node; c++, col++) {
00331       // calculate column for +epsilon
00332       delta(c) = epsilon;
00333       node->self_exmap(delta);
00334       int row = 0;
00335       for (list<Factor*>::const_iterator it_factor = node->factors().begin();
00336           it_factor!=node->factors().end();
00337           it_factor++) {
00338         Factor* factor = *it_factor;
00339         int dim_factor = factor->dim();
00340         y_plus.segment(row, dim_factor) = factor->evaluate();
00341         row += dim_factor;
00342       }
00343       node->update0(original);
00344       // calculate column for -epsilon
00345       delta(c) = - epsilon;
00346       node->self_exmap(delta);
00347       row = 0;
00348       for (list<Factor*>::const_iterator it_factor = node->factors().begin();
00349           it_factor!=node->factors().end();
00350           it_factor++) {
00351         Factor* factor = *it_factor;
00352         int dim_factor = factor->dim();
00353         y_minus.segment(row, dim_factor) = factor->evaluate();
00354         row += dim_factor;
00355       }
00356       node->update0(original);
00357       delta(c) = 0.; // reusing delta
00358       // calculate derivative
00359       VectorXd diff = (y_plus.head(row) - y_minus.head(row)) / (epsilon + epsilon);
00360       // write entries into sparse Jacobian
00361       row = 0;
00362       int i = 0;
00363       for (list<Factor*>::const_iterator it_factor = (*it)->factors().begin();
00364           it_factor!=(*it)->factors().end();
00365           it_factor++, i++) {
00366         for (int r=0; r<(*it_factor)->dim(); r++, row++) {
00367           if (diff(row)!=0.) { // omit 0 entries
00368             int offset = (*it_factor)->_start;
00369             rows[offset+r]->append(col, diff(row)); // faster than SparseVector.set
00370           }
00371         }
00372       }
00373     }
00374   }
00375   VectorXd rhs = weighted_errors(LINPOINT);
00376   return SparseSystem(num_rows, _dim_nodes, rows, rhs);
00377 }
00378 
00379 SparseSystem Slam::jacobian() {
00380   if (_prop.force_numerical_jacobian) {
00381     // column-wise is more efficient, especially if some nodes are
00382     // connected to many factors
00383     return jacobian_numerical_columnwise();
00384   } else {
00385     // have to do row-wise if we want to use any available symbolic
00386     // derivatives
00387     return jacobian_partial(-1);
00388   }
00389 }
00390 
00391 const Covariances& Slam::covariances() {
00392   return _covariances;
00393 }
00394 
00395 SparseSystem Slam::jacobian_partial(int last_n) {
00396   update_starts();
00397   // actual assembly of Jacobian
00398   int num_rows = _dim_measure;
00399   if (last_n > 0) {
00400     num_rows = _num_new_rows;
00401   }
00402   DeleteOnReturn rows_ptr(new SparseVector*[num_rows]);
00403   SparseVector** rows = rows_ptr._ptr; //[num_rows];
00404 
00405   VectorXd rhs(num_rows);
00406   int row = 0;
00407   const list<Factor*>& factors = get_factors();
00408   list<Factor*>::const_iterator it = factors.begin();
00409   if (last_n != -1) {
00410     // skip all entries except for last_n
00411     for (int n = num_factors(); n>last_n; n--, it++);
00412   }
00413   for (; it!=factors.end(); it++) {
00414     Factor* factor = *it;
00415     Jacobian jac = factor->jacobian_internal(_prop.force_numerical_jacobian);
00416     VectorXd jac_rhs = jac.rhs();
00417     for (int r=0; r<jac_rhs.rows(); r++) {
00418       rhs(row+r) = jac_rhs(r);
00419       // do not delete, will be pulled into SparseSystem below
00420       rows[row+r] = new SparseVector(jac.dimtotal());
00421     }
00422     for (Terms::const_iterator it=jac.terms().begin(); it!=jac.terms().end(); it++) {
00423       int offset = it->node()->_start;
00424       int nr = it->term().rows();
00425       for (int r=0; r<nr; r++) { // 0-entries not omitted
00426         rows[row+r]->set(offset, it->term().row(r));
00427       }
00428     }
00429     row += factor->dim();
00430   }
00431   return SparseSystem(num_rows, _dim_nodes, rows, rhs);
00432 }
00433 
00434 void Slam::print_stats() {
00435   double nnz = _R.nnz();
00436   double max_per_col = _R.max_nz();
00437   double dim = _dim_nodes;
00438   double per_col = nnz/dim;
00439   double fill_in = nnz/(dim*dim);
00440   cout << "iSAM statistics:" << endl;
00441   cout << "  Normalized chi-square value: " << normalized_chi2() << endl;
00442   cout << "  Weighted sum of squared errors: " << chi2() << endl;
00443   cout << "  Number of nodes: " << num_nodes() << endl;
00444   cout << "  Number of factors: " << num_factors() << endl;
00445   cout << "  Number of variables: " << _dim_nodes << endl;
00446   cout << "  Number of measurements: " << _dim_measure << endl;
00447   cout << "  Number of non-zero entries: " << nnz << endl;
00448   cout << "    max per column: " << max_per_col << endl;
00449   cout << "    avg per column: " << per_col << endl;
00450   cout << "    fill in: " << fill_in << "%" << endl;
00451 }
00452 
00453 }


demo_rgbd
Author(s): Ji Zhang
autogenerated on Tue Mar 3 2015 18:01:08