Make class
Frank Dellaert
parent
90d3f2e84a
commit
18be91b917
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@ -46,243 +46,244 @@ using symbol_shorthand::X;
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// Testing params
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const size_t max_loop_count = 3000; // 2000; // 200 //2000 //8000
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noiseModel::Diagonal::shared_ptr prior_noise_model =
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noiseModel::Diagonal::Sigmas(
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(Vector(3) << 0.0001, 0.0001, 0.0001).finished());
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auto prior_noise_model = noiseModel::Diagonal::Sigmas(
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(Vector(3) << 0.0001, 0.0001, 0.0001).finished());
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noiseModel::Diagonal::shared_ptr pose_noise_model =
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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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auto 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 result of optimization to filename.
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*
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* @param result The Values object with the final result.
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* @param num_poses The number of poses to write to the file.
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* @param filename The file name to save the result to.
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*/
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void write_result(const Values& result, size_t num_poses,
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const std::string& filename = "Hybrid_city10000.txt") {
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ofstream outfile;
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outfile.open(filename);
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class Experiment {
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/**
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* @brief Write the result of optimization to file.
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*
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* @param result The Values object with the final result.
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* @param num_poses The number of poses to write to the file.
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* @param filename The file name to save the result to.
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*/
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void write_result(const Values& result, size_t num_poses,
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const std::string& filename = "Hybrid_city10000.txt") {
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ofstream outfile;
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outfile.open(filename);
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for (size_t i = 0; i < num_poses; ++i) {
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Pose2 out_pose = result.at<Pose2>(X(i));
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for (size_t i = 0; i < num_poses; ++i) {
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Pose2 out_pose = result.at<Pose2>(X(i));
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outfile << out_pose.x() << " " << out_pose.y() << " " << out_pose.theta()
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<< std::endl;
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}
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outfile.close();
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std::cout << "output written to " << filename << std::endl;
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}
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/**
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* @brief Create a hybrid loop closure factor where
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* 0 - loose noise model and 1 - loop noise model.
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*
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* @param loop_counter
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* @param key_s
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* @param key_t
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* @param measurement
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* @return HybridNonlinearFactor
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*/
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HybridNonlinearFactor HybridLoopClosureFactor(size_t loop_counter, size_t key_s,
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size_t key_t,
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const Pose2& measurement) {
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DiscreteKey l(L(loop_counter), 2);
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auto f0 = std::make_shared<BetweenFactor<Pose2>>(
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X(key_s), X(key_t), measurement,
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noiseModel::Diagonal::Sigmas(Vector3::Ones() * 100));
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auto f1 = std::make_shared<BetweenFactor<Pose2>>(
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X(key_s), X(key_t), measurement, pose_noise_model);
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std::vector<NonlinearFactorValuePair> factors{{f0, 0.0}, {f1, 0.0}};
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HybridNonlinearFactor mixtureFactor(l, {f0, f1});
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return mixtureFactor;
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}
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HybridNonlinearFactor HybridOdometryFactor(
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size_t num_measurements, size_t key_s, size_t key_t, const DiscreteKey& m,
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const std::vector<Pose2>& pose_array,
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const SharedNoiseModel& pose_noise_model) {
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auto f0 = std::make_shared<BetweenFactor<Pose2>>(
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X(key_s), X(key_t), pose_array[0], pose_noise_model);
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auto f1 = std::make_shared<BetweenFactor<Pose2>>(
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X(key_s), X(key_t), pose_array[1], pose_noise_model);
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std::vector<NonlinearFactorValuePair> factors{{f0, 0.0}, {f1, 0.0}};
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HybridNonlinearFactor mixtureFactor(m, factors);
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// HybridNonlinearFactor mixtureFactor(m, {f0, f1});
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return mixtureFactor;
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}
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void SmootherUpdate(HybridSmoother& smoother, HybridNonlinearFactorGraph& graph,
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const Values& initial, size_t maxNrHypotheses,
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Values* result) {
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HybridGaussianFactorGraph linearized = *graph.linearize(initial);
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// std::cout << "index: " << index << std::endl;
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smoother.update(linearized, maxNrHypotheses);
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graph.resize(0);
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HybridValues delta = smoother.hybridBayesNet().optimize();
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result->insert_or_assign(initial.retract(delta.continuous()));
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}
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/* ************************************************************************* */
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int main(int argc, char* argv[]) {
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ifstream in(findExampleDataFile("T1_city10000_04.txt"));
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// 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 discrete_count = 0, index = 0;
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size_t loop_count = 0;
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size_t nrHybridFactors = 0;
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std::list<double> time_list;
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HybridSmoother smoother(0.99);
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HybridNonlinearFactorGraph graph;
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Values initial, result;
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size_t maxNrHypotheses = 3;
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double x = 0.0;
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double y = 0.0;
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double rad = 0.0;
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Pose2 prior_pose(x, y, rad);
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initial.insert(X(0), prior_pose);
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graph.push_back(PriorFactor<Pose2>(X(0), prior_pose, prior_noise_model));
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std::vector<std::pair<size_t, double>> smoother_update_times;
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clock_t before_update = clock();
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SmootherUpdate(smoother, graph, initial, maxNrHypotheses, &result);
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clock_t after_update = clock();
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smoother_update_times.push_back({index, after_update - before_update});
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size_t key_s, key_t{0};
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clock_t start_time = clock();
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std::string str;
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while (getline(in, str) && index < max_loop_count) {
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vector<string> parts;
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split(parts, str, is_any_of(" "));
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key_s = stoi(parts[1]);
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key_t = stoi(parts[3]);
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int num_measurements = stoi(parts[5]);
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vector<Pose2> pose_array(num_measurements);
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for (int i = 0; i < num_measurements; ++i) {
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x = stod(parts[6 + 3 * i]);
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y = stod(parts[7 + 3 * i]);
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rad = stod(parts[8 + 3 * i]);
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pose_array[i] = Pose2(x, y, rad);
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outfile << out_pose.x() << " " << out_pose.y() << " " << out_pose.theta()
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<< std::endl;
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}
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outfile.close();
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std::cout << "output written to " << filename << std::endl;
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}
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// Flag if we should run smoother update
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bool smoother_update = false;
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/**
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* @brief Create a hybrid loop closure factor where
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* 0 - loose noise model and 1 - loop noise model.
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*/
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HybridNonlinearFactor HybridLoopClosureFactor(size_t loop_counter,
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size_t key_s, size_t key_t,
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const Pose2& measurement) {
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DiscreteKey l(L(loop_counter), 2);
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// Take the first one as the initial estimate
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Pose2 odom_pose = pose_array[0];
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if (key_s == key_t - 1) { // odometry
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auto f0 = std::make_shared<BetweenFactor<Pose2>>(
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X(key_s), X(key_t), measurement,
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noiseModel::Diagonal::Sigmas(Vector3::Ones() * 100));
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auto f1 = std::make_shared<BetweenFactor<Pose2>>(
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X(key_s), X(key_t), measurement, pose_noise_model);
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std::vector<NonlinearFactorValuePair> factors{{f0, 0.0}, {f1, 0.0}};
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HybridNonlinearFactor mixtureFactor(l, {f0, f1});
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return mixtureFactor;
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}
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if (num_measurements > 1) {
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DiscreteKey m(M(discrete_count), num_measurements);
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// Create hybrid odometry factor with discrete measurement choices
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HybridNonlinearFactor HybridOdometryFactor(
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size_t num_measurements, size_t key_s, size_t key_t, const DiscreteKey& m,
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const std::vector<Pose2>& pose_array,
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const SharedNoiseModel& pose_noise_model) {
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auto f0 = std::make_shared<BetweenFactor<Pose2>>(
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X(key_s), X(key_t), pose_array[0], pose_noise_model);
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auto f1 = std::make_shared<BetweenFactor<Pose2>>(
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X(key_s), X(key_t), pose_array[1], pose_noise_model);
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std::vector<NonlinearFactorValuePair> factors{{f0, 0.0}, {f1, 0.0}};
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HybridNonlinearFactor mixtureFactor(m, factors);
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// HybridNonlinearFactor mixtureFactor(m, {f0, f1});
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return mixtureFactor;
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}
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// Add hybrid factor which considers both measurements
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HybridNonlinearFactor mixtureFactor = HybridOdometryFactor(
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num_measurements, key_s, key_t, m, pose_array, pose_noise_model);
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graph.push_back(mixtureFactor);
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// Perform smoother update and optimize the graph
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void SmootherUpdate(HybridSmoother& smoother,
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HybridNonlinearFactorGraph& graph, const Values& initial,
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size_t maxNrHypotheses, Values* result) {
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HybridGaussianFactorGraph linearized = *graph.linearize(initial);
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// std::cout << "index: " << index << std::endl;
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smoother.update(linearized, maxNrHypotheses);
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graph.resize(0);
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HybridValues delta = smoother.hybridBayesNet().optimize();
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result->insert_or_assign(initial.retract(delta.continuous()));
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}
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discrete_count++;
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public:
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/// Construct with filename of experiment to run
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Experiment(const std::string& filename) {
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ifstream in(filename);
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size_t discrete_count = 0, index = 0;
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size_t loop_count = 0;
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size_t nrHybridFactors = 0;
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std::list<double> time_list;
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HybridSmoother smoother(0.99);
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HybridNonlinearFactorGraph graph;
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Values initial, result;
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size_t maxNrHypotheses = 3;
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double x = 0.0;
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double y = 0.0;
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double rad = 0.0;
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Pose2 prior_pose(x, y, rad);
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initial.insert(X(0), prior_pose);
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graph.push_back(PriorFactor<Pose2>(X(0), prior_pose, prior_noise_model));
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std::vector<std::pair<size_t, double>> smoother_update_times;
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clock_t before_update = clock();
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SmootherUpdate(smoother, graph, initial, maxNrHypotheses, &result);
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clock_t after_update = clock();
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smoother_update_times.push_back({index, after_update - before_update});
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size_t key_s, key_t{0};
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clock_t start_time = clock();
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std::string str;
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while (getline(in, str) && index < max_loop_count) {
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vector<string> parts;
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split(parts, str, is_any_of(" "));
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key_s = stoi(parts[1]);
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key_t = stoi(parts[3]);
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int num_measurements = stoi(parts[5]);
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vector<Pose2> pose_array(num_measurements);
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for (int i = 0; i < num_measurements; ++i) {
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x = stod(parts[6 + 3 * i]);
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y = stod(parts[7 + 3 * i]);
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rad = stod(parts[8 + 3 * i]);
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pose_array[i] = Pose2(x, y, rad);
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}
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// Flag if we should run smoother update
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bool smoother_update = false;
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// Take the first one as the initial estimate
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Pose2 odom_pose = pose_array[0];
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if (key_s == key_t - 1) { // odometry
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if (num_measurements > 1) {
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DiscreteKey m(M(discrete_count), num_measurements);
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// Add hybrid factor which considers both measurements
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HybridNonlinearFactor mixtureFactor = HybridOdometryFactor(
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num_measurements, key_s, key_t, m, pose_array, pose_noise_model);
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graph.push_back(mixtureFactor);
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discrete_count++;
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smoother_update = true;
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} else {
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graph.add(BetweenFactor<Pose2>(X(key_s), X(key_t), odom_pose,
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pose_noise_model));
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}
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initial.insert(X(key_t), initial.at<Pose2>(X(key_s)) * odom_pose);
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} else { // loop
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HybridNonlinearFactor loop_factor =
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HybridLoopClosureFactor(loop_count, key_s, key_t, odom_pose);
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graph.add(loop_factor);
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smoother_update = true;
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} else {
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graph.add(BetweenFactor<Pose2>(X(key_s), X(key_t), odom_pose,
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pose_noise_model));
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loop_count++;
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}
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initial.insert(X(key_t), initial.at<Pose2>(X(key_s)) * odom_pose);
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if (smoother_update) {
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gttic_(SmootherUpdate);
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before_update = clock();
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SmootherUpdate(smoother, graph, initial, maxNrHypotheses, &result);
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after_update = clock();
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smoother_update_times.push_back({index, after_update - before_update});
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gttoc_(SmootherUpdate);
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}
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} else { // loop
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HybridNonlinearFactor loop_factor =
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HybridLoopClosureFactor(loop_count, key_s, key_t, odom_pose);
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graph.add(loop_factor);
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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) {
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if (index % 100 == 0) {
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std::cout << "index: " << index << std::endl;
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std::cout << "acc_time: " << time_list.back() / CLOCKS_PER_SEC
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<< std::endl;
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// delta.discrete().print("The Discrete Assignment");
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tictoc_finishedIteration_();
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tictoc_print_();
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}
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smoother_update = true;
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if (key_s == key_t - 1) {
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clock_t cur_time = clock();
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time_list.push_back(cur_time - start_time);
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}
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loop_count++;
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index += 1;
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}
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if (smoother_update) {
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gttic_(SmootherUpdate);
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before_update = clock();
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SmootherUpdate(smoother, graph, initial, maxNrHypotheses, &result);
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after_update = clock();
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smoother_update_times.push_back({index, after_update - before_update});
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gttoc_(SmootherUpdate);
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}
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before_update = clock();
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SmootherUpdate(smoother, graph, initial, maxNrHypotheses, &result);
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after_update = clock();
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smoother_update_times.push_back({index, after_update - before_update});
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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) {
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if (index % 100 == 0) {
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std::cout << "index: " << index << std::endl;
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std::cout << "acc_time: " << time_list.back() / CLOCKS_PER_SEC
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<< std::endl;
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// delta.discrete().print("The Discrete Assignment");
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tictoc_finishedIteration_();
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tictoc_print_();
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}
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gttic_(HybridSmootherOptimize);
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HybridValues delta = smoother.hybridBayesNet().optimize();
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gttoc_(HybridSmootherOptimize);
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result.insert_or_assign(initial.retract(delta.continuous()));
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if (key_s == key_t - 1) {
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clock_t cur_time = clock();
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time_list.push_back(cur_time - start_time);
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}
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std::cout << "Final error: " << smoother.hybridBayesNet().error(delta)
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<< std::endl;
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clock_t end_time = clock();
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clock_t total_time = end_time - start_time;
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cout << "total_time: " << total_time / CLOCKS_PER_SEC << " seconds" << endl;
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index += 1;
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/// Write result to file
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write_result(result, (key_t + 1), "Hybrid_City10000.txt");
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// TODO Write to file
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// for (size_t i = 0; i < smoother_update_times.size(); i++) {
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// auto p = smoother_update_times.at(i);
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// std::cout << p.first << ", " << p.second / CLOCKS_PER_SEC <<
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// std::endl;
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// }
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ofstream outfile_time;
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std::string time_file_name = "Hybrid_City10000_time.txt";
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outfile_time.open(time_file_name);
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for (auto acc_time : time_list) {
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outfile_time << acc_time << std::endl;
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}
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outfile_time.close();
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cout << "output " << time_file_name << " file." << endl;
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std::cout << nrHybridFactors << std::endl;
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}
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};
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before_update = clock();
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SmootherUpdate(smoother, graph, initial, maxNrHypotheses, &result);
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after_update = clock();
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smoother_update_times.push_back({index, after_update - before_update});
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gttic_(HybridSmootherOptimize);
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HybridValues delta = smoother.hybridBayesNet().optimize();
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gttoc_(HybridSmootherOptimize);
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result.insert_or_assign(initial.retract(delta.continuous()));
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std::cout << "Final error: " << smoother.hybridBayesNet().error(delta)
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<< std::endl;
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clock_t end_time = clock();
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clock_t total_time = end_time - start_time;
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cout << "total_time: " << total_time / CLOCKS_PER_SEC << " seconds" << endl;
|
||||
|
||||
/// Write result to file
|
||||
write_result(result, (key_t + 1), "Hybrid_City10000.txt");
|
||||
|
||||
//TODO Write to file
|
||||
// for (size_t i = 0; i < smoother_update_times.size(); i++) {
|
||||
// auto p = smoother_update_times.at(i);
|
||||
// std::cout << p.first << ", " << p.second / CLOCKS_PER_SEC << std::endl;
|
||||
// }
|
||||
ofstream outfile_time;
|
||||
std::string time_file_name = "Hybrid_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;
|
||||
std::cout << nrHybridFactors << std::endl;
|
||||
/* ************************************************************************* */
|
||||
int main(int argc, char* argv[]) {
|
||||
Experiment(findExampleDataFile("T1_city10000_04.txt"));
|
||||
// Experiment("../data/mh_T1_city10000_04.txt"); //Type #1 only
|
||||
// Experiment("../data/mh_T3b_city10000_10.txt"); //Type #3 only
|
||||
// Experiment("../data/mh_T1_T3_city10000_04.txt"); //Type #1 + Type #3
|
||||
|
||||
// Experiment("../data/mh_All_city10000_groundtruth.txt");
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue