gtsam/gtsam_unstable/examples/SmartProjectionFactorExampl...

433 lines
15 KiB
C++

/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file SmartProjectionFactorExample_kitti.cpp
* @brief Example usage of SmartProjectionFactor using real dataset in a non-batch fashion
* @date August, 2013
* @author Zsolt Kira
*/
// Use a map to store landmark/smart factor pairs
#include <gtsam/base/FastMap.h>
// Both relative poses and recovered trajectory poses will be stored as Pose3 objects
#include <gtsam/geometry/Pose3.h>
// Each variable in the system (poses and landmarks) must be identified with a unique key.
// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
// Here we will use Symbols
#include <gtsam/inference/Symbol.h>
// We want to use iSAM2 to solve the range-SLAM problem incrementally
#include <gtsam/nonlinear/ISAM2.h>
// iSAM2 requires as input a set set of new factors to be added stored in a factor graph,
// and initial guesses for any new variables used in the added factors
#include <gtsam/nonlinear/Values.h>
// We will use a non-linear solver to batch-initialize from the first 150 frames
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
// In GTSAM, measurement functions are represented as 'factors'. Several common factors
// have been provided with the library for solving robotics SLAM problems.
#include <gtsam/slam/PriorFactor.h>
#include <gtsam_unstable/slam/SmartProjectionFactorsCreator.h>
#include <gtsam_unstable/slam/GenericProjectionFactorsCreator.h>
// Standard headers, added last, so we know headers above work on their own
#include <boost/foreach.hpp>
#include <boost/assign.hpp>
#include <boost/assign/std/vector.hpp>
#include <fstream>
#include <iostream>
using namespace std;
using namespace gtsam;
using namespace boost::assign;
namespace NM = gtsam::noiseModel;
using symbol_shorthand::X;
using symbol_shorthand::L;
typedef PriorFactor<Pose3> Pose3Prior;
typedef SmartProjectionFactorsCreator<Pose3, Point3, Cal3_S2> SmartFactorsCreator;
typedef GenericProjectionFactorsCreator<Pose3, Point3, Cal3_S2> ProjectionFactorsCreator;
typedef FastMap<Key, int> OrderingMap;
bool debug = false;
//// Helper functions taken from VO code
// Loaded all pose values into list
Values::shared_ptr loadPoseValues(const string& filename) {
Values::shared_ptr values(new Values());
bool addNoise = false;
std::cout << "PARAM Noise: " << addNoise << std::endl;
// Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::Point3(0.5,0.1,0.3));
Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/100, 0., -M_PI/100), gtsam::Point3(0.3,0.1,0.3));
// read in camera poses
string full_filename = filename;
ifstream fin;
fin.open(full_filename.c_str());
int pose_id;
while (fin >> pose_id) {
double pose_matrix[16];
for (int i = 0; i < 16; i++) {
fin >> pose_matrix[i];
}
if (addNoise) {
values->insert(Symbol('x',pose_id), Pose3(Matrix_(4, 4, pose_matrix)).compose(noise_pose));
} else {
values->insert(Symbol('x',pose_id), Pose3(Matrix_(4, 4, pose_matrix)));
}
}
fin.close();
return values;
}
// Load specific pose values that are in key list
Values::shared_ptr loadPoseValues(const string& filename, list<Key> keys) {
Values::shared_ptr values(new Values());
std::list<Key>::iterator kit;
// read in camera poses
string full_filename = filename;
ifstream fin;
fin.open(full_filename.c_str());
int pose_id;
while (fin >> pose_id) {
double pose_matrix[16];
for (int i = 0; i < 16; i++) {
fin >> pose_matrix[i];
}
kit = find (keys.begin(), keys.end(), X(pose_id));
if (kit != keys.end()) {
//cout << " Adding " << X(pose_id) << endl;
values->insert(Symbol('x',pose_id), Pose3(Matrix_(4, 4, pose_matrix)));
}
}
fin.close();
return values;
}
// Load calibration info
Cal3_S2::shared_ptr loadCalibration(const string& filename) {
string full_filename = filename;
ifstream fin;
fin.open(full_filename.c_str());
// try loading from parent directory as backup
if(!fin) {
cerr << "Could not load " << full_filename;
exit(1);
}
double fx, fy, s, u, v, b;
fin >> fx >> fy >> s >> u >> v >> b;
fin.close();
Cal3_S2::shared_ptr K(new Cal3_S2(fx, fy, s, u, v));
return K;
}
// Write key values to file
void writeValues(string directory_, const Values& values){
string filename = directory_ + "out_camera_poses.txt";
ofstream fout;
fout.open(filename.c_str());
fout.precision(20);
// write out camera poses
BOOST_FOREACH(Values::ConstFiltered<Pose3>::value_type key_value, values.filter<Pose3>()) {
fout << Symbol(key_value.key).index();
const gtsam::Matrix& matrix= key_value.value.matrix();
for (size_t row=0; row < 4; ++row) {
for (size_t col=0; col < 4; ++col) {
fout << " " << matrix(row, col);
}
}
fout << endl;
}
fout.close();
if(values.filter<Point3>().size() > 0) {
// write landmarks
filename = directory_ + "landmarks.txt";
fout.open(filename.c_str());
BOOST_FOREACH(Values::ConstFiltered<Point3>::value_type key_value, values.filter<Point3>()) {
fout << Symbol(key_value.key).index();
fout << " " << key_value.value.x();
fout << " " << key_value.value.y();
fout << " " << key_value.value.z();
fout << endl;
}
fout.close();
}
}
void optimizeGraphLM(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr graphValues, Values &result, boost::shared_ptr<Ordering> &ordering) {
// Optimization parameters
LevenbergMarquardtParams params;
params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
params.verbosity = NonlinearOptimizerParams::ERROR;
params.lambdaInitial = 1;
params.lambdaFactor = 10;
// Profile a single iteration
// params.maxIterations = 1;
params.maxIterations = 100;
std::cout << " LM max iterations: " << params.maxIterations << std::endl;
// // params.relativeErrorTol = 1e-5;
params.absoluteErrorTol = 1.0;
params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
params.verbosity = NonlinearOptimizerParams::ERROR;
params.linearSolverType = SuccessiveLinearizationParams::MULTIFRONTAL_CHOLESKY;
cout << "Graph size: " << graph.size() << endl;
cout << "Number of variables: " << graphValues->size() << endl;
std::cout << " OPTIMIZATION " << std::endl;
std::cout << "\n\n=================================================\n\n";
if (debug) {
graph.print("thegraph");
}
std::cout << "\n\n=================================================\n\n";
if (ordering && ordering->size() > 0) {
if (debug) {
std::cout << "Have an ordering\n" << std::endl;
BOOST_FOREACH(const Key& key, *ordering) {
std::cout << key << " ";
}
std::cout << std::endl;
}
params.ordering = *ordering;
//for (int i = 0; i < 3; i++) {
LevenbergMarquardtOptimizer optimizer(graph, *graphValues, params);
gttic_(GenericProjectionFactorExample_kitti);
result = optimizer.optimize();
gttoc_(GenericProjectionFactorExample_kitti);
tictoc_finishedIteration_();
//}
} else {
std::cout << "Using COLAMD ordering\n" << std::endl;
//boost::shared_ptr<Ordering> ordering2(new Ordering()); ordering = ordering2;
//for (int i = 0; i < 3; i++) {
LevenbergMarquardtOptimizer optimizer(graph, *graphValues, params);
params.ordering = Ordering::COLAMD(graph);
gttic_(SmartProjectionFactorExample_kitti);
result = optimizer.optimize();
gttoc_(SmartProjectionFactorExample_kitti);
tictoc_finishedIteration_();
//}
//*ordering = params.ordering;
if (params.ordering) {
std::cout << "Graph size: " << graph.size() << " ORdering: " << params.ordering->size() << std::endl;
ordering = boost::make_shared<Ordering>(*(new Ordering()));
*ordering = *params.ordering;
} else {
std::cout << "WARNING: NULL ordering!" << std::endl;
}
}
}
void optimizeGraphGN(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr graphValues, Values &result) {
GaussNewtonParams params;
//params.maxIterations = 1;
params.verbosity = NonlinearOptimizerParams::DELTA;
GaussNewtonOptimizer optimizer(graph, *graphValues, params);
gttic_(SmartProjectionFactorExample_kitti);
result = optimizer.optimize();
gttoc_(SmartProjectionFactorExample_kitti);
tictoc_finishedIteration_();
}
void optimizeGraphISAM2(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr graphValues, Values &result) {
ISAM2 isam;
gttic_(SmartProjectionFactorExample_kitti);
isam.update(graph, *graphValues);
result = isam.calculateEstimate();
gttoc_(SmartProjectionFactorExample_kitti);
tictoc_finishedIteration_();
}
// main
int main(int argc, char** argv) {
unsigned int maxNumLandmarks = 10000; //389007; //100000000; // 309393 // (loop_closure_merged) //37106 //(reduced kitti);
unsigned int maxNumPoses = 1e+6;
// Set to true to use SmartProjectionFactor. Otherwise GenericProjectionFactor will be used
bool useSmartProjectionFactor = false;
bool useLM = true;
double KittiLinThreshold = -1.0; // 0.005; //
double KittiRankTolerance = 1.0;
bool incrementalFlag = false;
int optSkip = 200; // we optimize the graph every optSkip poses
std::cout << "PARAM SmartFactor: " << useSmartProjectionFactor << std::endl;
std::cout << "PARAM LM: " << useLM << std::endl;
std::cout << "PARAM KittiLinThreshold (negative is disabled): " << KittiLinThreshold << std::endl;
// Get home directory and dataset
string HOME = getenv("HOME");
//string input_dir = HOME + "/data/KITTI_00_200/";
string input_dir = HOME + "/data/kitti/loop_closures_merged/"; // 399997 landmarks, 4541 poses
//string input_dir = HOME + "/data/kitti_00_full_dirty/";
static SharedNoiseModel pixel_sigma(noiseModel::Unit::Create(2));
NonlinearFactorGraph graphSmart, graphProjection;
// Load calibration
boost::shared_ptr<Cal3_S2> K = loadCalibration(input_dir+"calibration.txt");
K->print("Calibration");
// Read in kitti dataset
ifstream fin;
fin.open((input_dir+"stereo_factors.txt").c_str());
if(!fin) {
cerr << "Could not open stereo_factors.txt" << endl;
exit(1);
}
// Load all values
gtsam::Values::shared_ptr graphSmartValues(new gtsam::Values());
gtsam::Values::shared_ptr graphProjectionValues(new gtsam::Values());
gtsam::Values::shared_ptr loadedValues = loadPoseValues(input_dir+"camera_poses.txt");
// read all measurements tracked by VO stereo
cout << "Loading stereo_factors.txt" << endl;
unsigned int count = 0;
Key currentLandmark = 0;
unsigned int numLandmarks = 0, numPoses = 0;
Key r, l;
double uL, uR, v, x, y, z;
std::vector<Key> landmarkKeys, cameraPoseKeys;
Values values;
Values result;
bool optimized = false;
boost::shared_ptr<Ordering> ordering(new Ordering());
bool breakingCondition;
SmartFactorsCreator smartCreator(pixel_sigma, K, KittiRankTolerance, KittiLinThreshold);
ProjectionFactorsCreator projectionCreator(pixel_sigma, K);
// main loop: reads measurements and adds factors (also performs optimization if desired)
// r >> pose id
// l >> landmark id
// (uL >> uR) >> measurement (xaxis pixel measurement in left and right camera - since we do monocular, we only use uL)
// v >> measurement (yaxis pixel measurement)
// (x >> y >> z) 3D initialization for the landmark (not used in this code)
while (fin >> r >> l >> uL >> uR >> v >> x >> y >> z) {
if (debug) fprintf(stderr,"Landmark %ld\n", l);
if (debug) fprintf(stderr,"Line %d: %d landmarks, (max landmarks %d), %d poses, max poses %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
// 1: add values and factors to the graph
// 1.1: add factors
// SMART FACTORS ..
if (useSmartProjectionFactor) {
smartCreator.add(L(l), X(r), Point2(uL,v), graphSmart);
numLandmarks = smartCreator.getNumLandmarks();
// Add initial pose value if pose does not exist
if (!graphSmartValues->exists<Pose3>(X(r)) && loadedValues->exists<Pose3>(X(r))) {
graphSmartValues->insert(X(r), loadedValues->at<Pose3>(X(r)));
numPoses++;
optimized = false;
}
} else {
// or STANDARD PROJECTION FACTORS
projectionCreator.add(L(l), X(r), Point2(uL,v), graphProjection);
numLandmarks = projectionCreator.getNumLandmarks();
optimized = false;
}
breakingCondition = currentLandmark != l && (numPoses > maxNumPoses || numLandmarks > maxNumLandmarks);
// Optimize if have a certain number of poses/landmarks, or we want to do incremental inference
if (breakingCondition || (incrementalFlag && !optimized && ((numPoses+1) % optSkip)==0) ) {
if (debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
if (debug) cout << "Adding triangulated landmarks, graph size: " << graphProjection.size() << endl;
if (useSmartProjectionFactor == false) {
projectionCreator.update(graphProjection, loadedValues, graphProjectionValues);
ordering = projectionCreator.getOrdering();
}
if (debug) cout << "Adding triangulated landmarks, graph size after: " << graphProjection.size() << endl;
if (1||debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
if (useSmartProjectionFactor) {
if (useLM)
optimizeGraphLM(graphSmart, graphSmartValues, result, ordering);
else
optimizeGraphISAM2(graphSmart, graphSmartValues, result);
} else {
if (useLM)
optimizeGraphLM(graphProjection, graphProjectionValues, result, ordering);
else
optimizeGraphISAM2(graphSmart, graphSmartValues, result);
}
if(incrementalFlag) *graphSmartValues = result; // we use optimized solution as initial guess for the next one
optimized = true;
}
if (debug) fprintf(stderr,"%d %d\n", count, maxNumLandmarks);
if (debug) cout << "CurrentLandmark " << currentLandmark << " Landmark " << l << std::endl;
if (1||debug) fprintf(stderr,"%d: %d, %d > %d, %d > %d\n", count, currentLandmark != l, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
if(breakingCondition){ // reached desired number of landmarks/poses
break;
}
currentLandmark = l;
count++;
if(count==100000) {
cout << "Loading graph smart... " << graphSmart.size() << endl;
cout << "Loading graph projection... " << graphProjection.size() << endl;
}
} // end of while
if (1||debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
cout << "===================================================" << endl;
//graphSmartValues->print("before optimization ");
//result.print("results of kitti optimization ");
tictoc_print_();
cout << "===================================================" << endl;
writeValues("./", result);
if (1||debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
exit(0);
}