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/* ----------------------------------------------------------------------------
* 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
* @date August, 2013
* @author Zsolt Kira
*/
// Both relative poses and recovered trajectory poses will be stored as Pose2 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/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
// We will use a non-liear solver to batch-inituialize from the first 150 frames
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.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/slam/ProjectionFactor.h>
#include <gtsam_unstable/slam/SmartProjectionFactor.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;
//// 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));
// 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;
}
// Loaded 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;
}
void writeValues(string directory_, const Values& values){
string filename = directory_ + "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();
}
}
// main
int main(int argc, char** argv) {
bool debug = false;
// Set to true to use SmartProjectionFactor. Otherwise GenericProjectionFactor will be used
bool useSmartProjectionFactor = true;
std::cout << "PARAM SmartFactor: " << useSmartProjectionFactor << std::endl;
// Minimum number of views of a landmark before it is added to the graph (SmartProjectionFactor case only)
unsigned int minimumNumViews = 1;
string HOME = getenv("HOME");
//string input_dir = HOME + "/data/kitti/loop_closures_merged/";
string input_dir = HOME + "/data/KITTI_00_200/";
typedef SmartProjectionFactor<Pose3, Point3, Cal3_S2> SmartFactor;
typedef GenericProjectionFactor<Pose3, Point3, Cal3_S2> ProjectionFactor;
static SharedNoiseModel pixel_sigma(noiseModel::Unit::Create(2));
static SharedNoiseModel prior_model(noiseModel::Diagonal::Sigmas(Vector_(6, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01)));
NonlinearFactorGraph graph;
// Load calibration
//Cal3_S2::shared_ptr K(new Cal3_S2(718.856, 718.856, 0.0, 607.1928, 185.2157));
boost::shared_ptr<Cal3_S2> K = loadCalibration(input_dir+"calibration.txt");
K->print("Calibration");
// Load values from VO camera poses output
gtsam::Values::shared_ptr loaded_values = loadPoseValues(input_dir+"camera_poses.txt");
graph.push_back(Pose3Prior(X(0),loaded_values->at<Pose3>(X(0)), prior_model));
graph.push_back(Pose3Prior(X(1),loaded_values->at<Pose3>(X(1)), prior_model));
//graph.print("thegraph");
// 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);
}
// read all measurements tracked by VO stereo
cout << "Loading stereo_factors.txt" << endl;
int count = 0;
Key currentLandmark = 0;
int numLandmarks = 0;
Key r, l;
double uL, uR, v, x, y, z;
std::list<Key> allViews;
std::vector<Key> views;
std::vector<Point2> measurements;
Values values;
while (fin >> r >> l >> uL >> uR >> v >> x >> y >> z) {
if (debug) cout << "CurrentLandmark " << currentLandmark << " Landmark " << l << std::endl;
if (useSmartProjectionFactor == false) {
if (loaded_values->exists<Point3>(L(l)) == boost::none) {
Pose3 camera = loaded_values->at<Pose3>(X(r));
Point3 worldPoint = camera.transform_from(Point3(x, y, z));
loaded_values->insert(L(l), worldPoint); // add point;
}
ProjectionFactor::shared_ptr projectionFactor(new ProjectionFactor(Point2(uL,v), pixel_sigma, X(r), L(l), K));
graph.push_back(projectionFactor);
}
if (currentLandmark != l && views.size() < minimumNumViews) {
// New landmark. Not enough views for previous landmark so move on.
if (debug) cout << "New landmark " << l << " with not enough view for previous one" << std::endl;
currentLandmark = l;
views.clear();
measurements.clear();
} else if (currentLandmark != l) {
// New landmark. Add previous landmark and associated views to new factor
if (debug) cout << "New landmark " << l << " with "<< views.size() << " views for previous landmark " << currentLandmark << std::endl;
if (debug) cout << "Keys ";
BOOST_FOREACH(const Key& k, views) {
allViews.push_back(k);
if (debug) cout << k << " ";
}
if (debug) cout << endl;
if (debug) {
cout << "Measurements ";
BOOST_FOREACH(const Point2& p, measurements) {
cout << p << " ";
}
cout << endl;
}
if (useSmartProjectionFactor) {
SmartFactor::shared_ptr smartFactor(new SmartFactor(measurements, pixel_sigma, views, K));
graph.push_back(smartFactor);
}
numLandmarks++;
currentLandmark = l;
views.clear();
measurements.clear();
} else {
// We have new view for current landmark, so add it to the list later
if (debug) cout << "New view for landmark " << l << " (" << views.size() << " total)" << std::endl;
}
// Add view for new landmark
views += X(r);
measurements += Point2(uL,v);
count++;
if(count==100000) {
cout << "Loading graph... " << graph.size() << endl;
count=0;
}
}
// Add last measurements
if (useSmartProjectionFactor) {
SmartFactor::shared_ptr smartFactor(new SmartFactor(measurements, pixel_sigma, views, K));
graph.push_back(smartFactor);
}
cout << "Graph size: " << graph.size() << endl;
/*
// If using only subset of graph, only read in values for keys that are used
// Get all view in the graph and populate poses from VO output
// TODO: Handle loop closures properly
cout << "All Keys (" << allViews.size() << ")" << endl;
allViews.unique();
cout << "All Keys (" << allViews.size() << ")" << endl;
values = *loadPoseValues(input_dir+"camera_poses.txt", allViews);
BOOST_FOREACH(const Key& k, allViews) {
if (debug) cout << k << " ";
}
cout << endl;
*/
cout << "Optimizing... " << endl;
// Optimize!
LevenbergMarquardtParams params;
params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
params.verbosity = NonlinearOptimizerParams::ERROR;
params.lambdaInitial = 1;
params.lambdaFactor = 10;
params.maxIterations = 100;
params.relativeErrorTol = 1e-5;
params.absoluteErrorTol = 1.0;
params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
params.verbosity = NonlinearOptimizerParams::ERROR;
params.linearSolverType = SuccessiveLinearizationParams::MULTIFRONTAL_CHOLESKY;
Values result;
for (int i = 0; i < 1; i++) {
std::cout << " OPTIMIZATION " << i << std::endl;
LevenbergMarquardtOptimizer optimizer(graph, *loaded_values, params);
gttic_(SmartProjectionFactorExample_kitti);
result = optimizer.optimize();
gttoc_(SmartProjectionFactorExample_kitti);
tictoc_finishedIteration_();
}
cout << "===================================================" << endl;
loaded_values->print("before optimization ");
result.print("results of kitti optimization ");
tictoc_print_();
cout << "===================================================" << endl;
writeValues("./", result);
exit(0);
}
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