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										 |  |  | /* ----------------------------------------------------------------------------
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							|  |  |  |  * 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) | 
					
						
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							|  |  |  |  * See LICENSE for the license information | 
					
						
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							|  |  |  |  * -------------------------------------------------------------------------- */ | 
					
						
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							|  |  |  | /**
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							|  |  |  |  * @file   Hybrid_City10000.cpp | 
					
						
							|  |  |  |  * @brief  Example of using hybrid estimation | 
					
						
							|  |  |  |  *         with multiple odometry measurements. | 
					
						
							|  |  |  |  * @author Varun Agrawal | 
					
						
							|  |  |  |  * @date   January 22, 2025 | 
					
						
							|  |  |  |  */ | 
					
						
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							|  |  |  | #include <gtsam/geometry/Pose2.h>
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							|  |  |  | #include <gtsam/inference/Symbol.h>
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							|  |  |  | #include <gtsam/nonlinear/ISAM2.h>
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							|  |  |  | #include <gtsam/nonlinear/NonlinearFactorGraph.h>
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							|  |  |  | #include <gtsam/nonlinear/Values.h>
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							|  |  |  | #include <gtsam/slam/BetweenFactor.h>
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							|  |  |  | #include <gtsam/slam/dataset.h>
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							|  |  |  | #include <time.h>
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							|  |  |  | #include <boost/algorithm/string/classification.hpp>
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							|  |  |  | #include <boost/algorithm/string/split.hpp>
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							|  |  |  | #include <fstream>
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							|  |  |  | #include <string>
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							|  |  |  | #include <vector>
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							|  |  |  | using namespace std; | 
					
						
							|  |  |  | using namespace gtsam; | 
					
						
							|  |  |  | using namespace boost::algorithm; | 
					
						
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							|  |  |  | using symbol_shorthand::X; | 
					
						
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							|  |  |  | // Testing params
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							|  |  |  | const size_t max_loop_count = 2000;  // 200 //2000 //8000
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							|  |  |  | const bool is_with_ambiguity = false;  // run original iSAM2 without ambiguities
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							|  |  |  | // const bool is_with_ambiguity = true;  // run original iSAM2 with ambiguities
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							|  |  |  | noiseModel::Diagonal::shared_ptr prior_noise_model = | 
					
						
							|  |  |  |     noiseModel::Diagonal::Sigmas( | 
					
						
							|  |  |  |         (Vector(3) << 0.0001, 0.0001, 0.0001).finished()); | 
					
						
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							|  |  |  | noiseModel::Diagonal::shared_ptr pose_noise_model = | 
					
						
							|  |  |  |     noiseModel::Diagonal::Sigmas( | 
					
						
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										 |  |  |         (Vector(3) << 1.0 / 30.0, 1.0 / 30.0, 1.0 / 100.0).finished()); | 
					
						
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							|  |  |  | /**
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							|  |  |  |  * @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); | 
					
						
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							|  |  |  |   for (size_t i = 0; i < num_poses; ++i) { | 
					
						
							|  |  |  |     Pose2 out_pose = results.at<Pose2>(X(i)); | 
					
						
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							|  |  |  |     outfile << out_pose.x() << " " << out_pose.y() << " " << out_pose.theta() | 
					
						
							|  |  |  |             << std::endl; | 
					
						
							|  |  |  |   } | 
					
						
							|  |  |  |   outfile.close(); | 
					
						
							|  |  |  |   std::cout << "output written to " << filename << std::endl; | 
					
						
							|  |  |  | } | 
					
						
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							|  |  |  | /* ************************************************************************* */ | 
					
						
							|  |  |  | int main(int argc, char* argv[]) { | 
					
						
							|  |  |  |   ifstream in(findExampleDataFile("T1_city10000_04.txt")); | 
					
						
							|  |  |  |   // ifstream in("../data/mh_T1_city10000_04.txt"); //Type #1 only
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							|  |  |  |   // ifstream in("../data/mh_T3b_city10000_10.txt"); //Type #3 only
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							|  |  |  |   // ifstream in("../data/mh_T1_T3_city10000_04.txt"); //Type #1 + Type #3
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							|  |  |  |   // ifstream in("../data/mh_All_city10000_groundtruth.txt");
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							|  |  |  |   size_t pose_count = 0; | 
					
						
							|  |  |  |   size_t index = 0; | 
					
						
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							|  |  |  |   std::list<double> time_list; | 
					
						
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							|  |  |  |   ISAM2Params parameters; | 
					
						
							|  |  |  |   parameters.optimizationParams = gtsam::ISAM2GaussNewtonParams(0.0); | 
					
						
							|  |  |  |   parameters.relinearizeThreshold = 0.01; | 
					
						
							|  |  |  |   parameters.relinearizeSkip = 1; | 
					
						
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										 |  |  |   ISAM2* isam2 = new ISAM2(parameters); | 
					
						
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							|  |  |  |   NonlinearFactorGraph* graph = new NonlinearFactorGraph(); | 
					
						
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							|  |  |  |   Values init_values; | 
					
						
							|  |  |  |   Values results; | 
					
						
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							|  |  |  |   double x = 0.0; | 
					
						
							|  |  |  |   double y = 0.0; | 
					
						
							|  |  |  |   double rad = 0.0; | 
					
						
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							|  |  |  |   Pose2 prior_pose(x, y, rad); | 
					
						
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							|  |  |  |   init_values.insert(X(0), prior_pose); | 
					
						
							|  |  |  |   pose_count++; | 
					
						
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										 |  |  |   graph->addPrior<Pose2>(X(0), prior_pose, prior_noise_model); | 
					
						
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							|  |  |  |   isam2->update(*graph, init_values); | 
					
						
							|  |  |  |   graph->resize(0); | 
					
						
							|  |  |  |   init_values.clear(); | 
					
						
							|  |  |  |   results = isam2->calculateBestEstimate(); | 
					
						
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							|  |  |  |   //*
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							|  |  |  |   size_t key_s = 0; | 
					
						
							|  |  |  |   size_t key_t = 0; | 
					
						
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							|  |  |  |   clock_t start_time = clock(); | 
					
						
							|  |  |  |   string str; | 
					
						
							|  |  |  |   while (getline(in, str) && index < max_loop_count) { | 
					
						
							|  |  |  |     // cout << str << endl;
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							|  |  |  |     vector<string> parts; | 
					
						
							|  |  |  |     split(parts, str, is_any_of(" ")); | 
					
						
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							|  |  |  |     key_s = stoi(parts[1]); | 
					
						
							|  |  |  |     key_t = stoi(parts[3]); | 
					
						
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							|  |  |  |     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); | 
					
						
							|  |  |  |     } | 
					
						
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							|  |  |  |     Pose2 odom_pose; | 
					
						
							|  |  |  |     if (is_with_ambiguity) { | 
					
						
							|  |  |  |       // Get wrong intentionally
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							|  |  |  |       int id = index % num_measurements; | 
					
						
							|  |  |  |       odom_pose = Pose2(pose_array[id]); | 
					
						
							|  |  |  |     } else { | 
					
						
							|  |  |  |       odom_pose = pose_array[0]; | 
					
						
							|  |  |  |     } | 
					
						
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							|  |  |  |     if (key_s == key_t - 1) {  // new X(key)
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							|  |  |  |       init_values.insert(X(key_t), results.at<Pose2>(X(key_s)) * odom_pose); | 
					
						
							|  |  |  |       pose_count++; | 
					
						
							|  |  |  |     } else {  // loop
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							|  |  |  |       index++; | 
					
						
							|  |  |  |     } | 
					
						
							|  |  |  |     graph->add( | 
					
						
							|  |  |  |         BetweenFactor<Pose2>(X(key_s), X(key_t), odom_pose, pose_noise_model)); | 
					
						
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							|  |  |  |     isam2->update(*graph, init_values); | 
					
						
							|  |  |  |     graph->resize(0); | 
					
						
							|  |  |  |     init_values.clear(); | 
					
						
							|  |  |  |     results = isam2->calculateBestEstimate(); | 
					
						
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										 |  |  |     // Print loop index and time taken in processor clock ticks
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										 |  |  |     if (index % 50 == 0 && key_s != key_t - 1) { | 
					
						
							|  |  |  |       std::cout << "index: " << index << std::endl; | 
					
						
							|  |  |  |       std::cout << "acc_time:  " << time_list.back() << std::endl; | 
					
						
							|  |  |  |     } | 
					
						
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							|  |  |  |     if (key_s == key_t - 1) { | 
					
						
							|  |  |  |       clock_t cur_time = clock(); | 
					
						
							|  |  |  |       time_list.push_back(cur_time - start_time); | 
					
						
							|  |  |  |     } | 
					
						
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							|  |  |  |     if (time_list.size() % 100 == 0 && (key_s == key_t - 1)) { | 
					
						
							|  |  |  |       string step_file_idx = std::to_string(100000 + time_list.size()); | 
					
						
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							|  |  |  |       ofstream step_outfile; | 
					
						
							|  |  |  |       string step_file_name = "step_files/ISAM2_city10000_S" + step_file_idx; | 
					
						
							|  |  |  |       step_outfile.open(step_file_name + ".txt"); | 
					
						
							|  |  |  |       for (size_t i = 0; i < (key_t + 1); ++i) { | 
					
						
							|  |  |  |         Pose2 out_pose = results.at<Pose2>(X(i)); | 
					
						
							|  |  |  |         step_outfile << out_pose.x() << " " << out_pose.y() << " " | 
					
						
							|  |  |  |                      << out_pose.theta() << endl; | 
					
						
							|  |  |  |       } | 
					
						
							|  |  |  |       step_outfile.close(); | 
					
						
							|  |  |  |     } | 
					
						
							|  |  |  |   } | 
					
						
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							|  |  |  |   clock_t end_time = clock(); | 
					
						
							|  |  |  |   clock_t total_time = end_time - start_time; | 
					
						
							|  |  |  |   cout << "total_time: " << total_time << endl; | 
					
						
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							|  |  |  |   /// Write results to file
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							|  |  |  |   write_results(results, (key_t + 1)); | 
					
						
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							|  |  |  |   ofstream outfile_time; | 
					
						
							|  |  |  |   std::string time_file_name = "ISAM2_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; | 
					
						
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							|  |  |  |   return 0; | 
					
						
							|  |  |  | } |