220 lines
6.7 KiB
C++
220 lines
6.7 KiB
C++
/* ----------------------------------------------------------------------------
|
|
|
|
* GTSAM Copyright 2010-2020, 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 Hybrid_City10000.cpp
|
|
* @brief Example of using hybrid estimation
|
|
* with multiple odometry measurements.
|
|
* @author Varun Agrawal
|
|
* @date January 22, 2025
|
|
*/
|
|
|
|
#include <gtsam/geometry/Pose2.h>
|
|
#include <gtsam/hybrid/HybridNonlinearFactor.h>
|
|
#include <gtsam/hybrid/HybridNonlinearFactorGraph.h>
|
|
#include <gtsam/hybrid/HybridSmoother.h>
|
|
#include <gtsam/hybrid/HybridValues.h>
|
|
#include <gtsam/inference/Symbol.h>
|
|
#include <gtsam/nonlinear/Values.h>
|
|
#include <gtsam/slam/BetweenFactor.h>
|
|
#include <gtsam/slam/PriorFactor.h>
|
|
#include <gtsam/slam/dataset.h>
|
|
#include <time.h>
|
|
|
|
#include <boost/algorithm/string/classification.hpp>
|
|
#include <boost/algorithm/string/split.hpp>
|
|
#include <fstream>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
using namespace std;
|
|
using namespace gtsam;
|
|
using namespace boost::algorithm;
|
|
|
|
using symbol_shorthand::M;
|
|
using symbol_shorthand::X;
|
|
|
|
// Testing params
|
|
const size_t max_loop_count = 2000; // 2000; // 200 //2000 //8000
|
|
|
|
noiseModel::Diagonal::shared_ptr prior_noise_model =
|
|
noiseModel::Diagonal::Sigmas(
|
|
(Vector(3) << 0.0001, 0.0001, 0.0001).finished());
|
|
|
|
noiseModel::Diagonal::shared_ptr pose_noise_model =
|
|
noiseModel::Diagonal::Sigmas(
|
|
(Vector(3) << 1.0 / 30.0, 1.0 / 30.0, 1.0 / 100.0).finished());
|
|
|
|
/**
|
|
* @brief Write the results of optimization to filename.
|
|
*
|
|
* @param results The Values object with the final results.
|
|
* @param num_poses The number of poses to write to the file.
|
|
* @param filename The file name to save the results to.
|
|
*/
|
|
void write_results(const Values& results, size_t num_poses,
|
|
const std::string& filename = "ISAM2_city10000.txt") {
|
|
ofstream outfile;
|
|
outfile.open(filename);
|
|
|
|
for (size_t i = 0; i < num_poses; ++i) {
|
|
Pose2 out_pose = results.at<Pose2>(X(i));
|
|
|
|
outfile << out_pose.x() << " " << out_pose.y() << " " << out_pose.theta()
|
|
<< std::endl;
|
|
}
|
|
outfile.close();
|
|
std::cout << "output written to " << filename << std::endl;
|
|
}
|
|
|
|
void SmootherUpdate(HybridSmoother& smoother, HybridNonlinearFactorGraph& graph,
|
|
const Values& initial, size_t maxNrHypotheses,
|
|
Values* results) {
|
|
HybridGaussianFactorGraph linearized = *graph.linearize(initial);
|
|
// std::cout << "index: " << index << std::endl;
|
|
smoother.update(linearized, maxNrHypotheses);
|
|
graph.resize(0);
|
|
HybridValues delta = smoother.hybridBayesNet().optimize();
|
|
results->insert_or_assign(initial.retract(delta.continuous()));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main(int argc, char* argv[]) {
|
|
ifstream in(findExampleDataFile("T1_city10000_04.txt"));
|
|
// ifstream in("../data/mh_T1_city10000_04.txt"); //Type #1 only
|
|
// ifstream in("../data/mh_T3b_city10000_10.txt"); //Type #3 only
|
|
// ifstream in("../data/mh_T1_T3_city10000_04.txt"); //Type #1 + Type #3
|
|
|
|
// ifstream in("../data/mh_All_city10000_groundtruth.txt");
|
|
|
|
size_t discrete_count = 0, index = 0;
|
|
|
|
std::list<double> time_list;
|
|
|
|
HybridSmoother smoother(0.99);
|
|
|
|
HybridNonlinearFactorGraph graph;
|
|
|
|
Values init_values;
|
|
Values results;
|
|
|
|
size_t maxNrHypotheses = 3;
|
|
|
|
double x = 0.0;
|
|
double y = 0.0;
|
|
double rad = 0.0;
|
|
|
|
Pose2 prior_pose(x, y, rad);
|
|
|
|
init_values.insert(X(0), prior_pose);
|
|
|
|
graph.push_back(PriorFactor<Pose2>(X(0), prior_pose, prior_noise_model));
|
|
|
|
SmootherUpdate(smoother, graph, init_values, maxNrHypotheses, &results);
|
|
|
|
size_t key_s, key_t{0};
|
|
|
|
clock_t start_time = clock();
|
|
std::string str;
|
|
while (getline(in, str) && index < max_loop_count) {
|
|
vector<string> parts;
|
|
split(parts, str, is_any_of(" "));
|
|
|
|
key_s = stoi(parts[1]);
|
|
key_t = stoi(parts[3]);
|
|
|
|
int num_measurements = stoi(parts[5]);
|
|
vector<Pose2> pose_array(num_measurements);
|
|
for (int i = 0; i < num_measurements; ++i) {
|
|
x = stod(parts[6 + 3 * i]);
|
|
y = stod(parts[7 + 3 * i]);
|
|
rad = stod(parts[8 + 3 * i]);
|
|
pose_array[i] = Pose2(x, y, rad);
|
|
}
|
|
|
|
// Take the first one as the initial estimate
|
|
Pose2 odom_pose = pose_array[0];
|
|
if (key_s == key_t - 1) { // new X(key)
|
|
init_values.insert(X(key_t), init_values.at<Pose2>(X(key_s)) * odom_pose);
|
|
|
|
} else { // loop
|
|
// index++;
|
|
}
|
|
|
|
// Flag if we should run smoother update
|
|
bool smoother_update = false;
|
|
|
|
if (num_measurements == 2) {
|
|
// Add hybrid factor which considers both measurements
|
|
DiscreteKey m(M(discrete_count), num_measurements);
|
|
discrete_count++;
|
|
|
|
graph.push_back(DecisionTreeFactor(m, "0.6 0.4"));
|
|
|
|
auto f0 = std::make_shared<BetweenFactor<Pose2>>(
|
|
X(key_s), X(key_t), pose_array[0], pose_noise_model);
|
|
auto f1 = std::make_shared<BetweenFactor<Pose2>>(
|
|
X(key_s), X(key_t), pose_array[1], pose_noise_model);
|
|
std::vector<NonlinearFactorValuePair> factors{{f0, 0.0}, {f1, 0.0}};
|
|
// HybridNonlinearFactor mixtureFactor(m, factors);
|
|
HybridNonlinearFactor mixtureFactor(m, {f0, f1});
|
|
graph.push_back(mixtureFactor);
|
|
|
|
smoother_update = true;
|
|
|
|
} else {
|
|
graph.add(BetweenFactor<Pose2>(X(key_s), X(key_t), odom_pose,
|
|
pose_noise_model));
|
|
}
|
|
|
|
if (smoother_update) {
|
|
SmootherUpdate(smoother, graph, init_values, maxNrHypotheses, &results);
|
|
}
|
|
|
|
// Print loop index and time taken in processor clock ticks
|
|
// if (index % 50 == 0 && key_s != key_t - 1) {
|
|
if (index % 100 == 0) {
|
|
std::cout << "index: " << index << std::endl;
|
|
std::cout << "acc_time: " << time_list.back() / CLOCKS_PER_SEC << std::endl;
|
|
// delta.discrete().print("The Discrete Assignment");
|
|
tictoc_finishedIteration_();
|
|
tictoc_print_();
|
|
}
|
|
|
|
if (key_s == key_t - 1) {
|
|
clock_t cur_time = clock();
|
|
time_list.push_back(cur_time - start_time);
|
|
}
|
|
|
|
index += 1;
|
|
}
|
|
|
|
SmootherUpdate(smoother, graph, init_values, maxNrHypotheses, &results);
|
|
|
|
clock_t end_time = clock();
|
|
clock_t total_time = end_time - start_time;
|
|
cout << "total_time: " << total_time / CLOCKS_PER_SEC << " seconds" << endl;
|
|
|
|
/// Write results to file
|
|
write_results(results, (key_t + 1), "HybridISAM_city10000.txt");
|
|
|
|
ofstream outfile_time;
|
|
std::string time_file_name = "HybridISAM_city10000_time.txt";
|
|
outfile_time.open(time_file_name);
|
|
for (auto acc_time : time_list) {
|
|
outfile_time << acc_time << std::endl;
|
|
}
|
|
outfile_time.close();
|
|
cout << "output " << time_file_name << " file." << endl;
|
|
|
|
return 0;
|
|
}
|