IncrementalFixedLagSmootherExample.cpp

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/* ----------------------------------------------------------------------------
 
 * GTSAM Copyright 2010-2025, 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
 
 * -------------------------------------------------------------------------- */
 
// STL
#include <iostream>
#include <string>
// GTSAM
#include <gtsam/geometry/Pose3.h>
#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/IncrementalFixedLagSmoother.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/slam/dataset.h>  // for writeG2o
 
using namespace std;
using namespace gtsam;
// Shorthand for symbols
using symbol_shorthand::X;  // Pose3 (x,y,z, roll, pitch, yaw)
 
// Factor types
enum FactorType {
    PRIOR = 0,
    BETWEEN = 1
};
 
typedef Eigen::Matrix<double, 6, 6> Matrix6;
 
/* ************************************************************************* */
Matrix6 readCovarianceMatrix(istringstream &iss) {
    Matrix6 cov;
    for (int r = 0; r < 6; ++r) {
        for (int c = 0; c < 6; ++c) {
            iss >> cov(r, c);
        }
    }
    return cov;
}
 
/* ************************************************************************* */
Pose3 createPose(double x, double y, double z, double roll, double pitch, double yaw) {
    return Pose3(Rot3::RzRyRx(roll, pitch, yaw), Point3(x, y, z));
}
 
/* ************************************************************************* */
void saveG2oGraph(const NonlinearFactorGraph &graph, const Values &estimate,
                  const string &filename, int iterCount) {
    // Create zero-padded iteration count
    string countStr = to_string(iterCount);
    string paddedCount = string(5 - countStr.length(), '0') + countStr;
    string fullFilename = filename + "_" + paddedCount + ".g2o";
    // Write graph and estimates to g2o file
    writeG2o(graph, estimate, fullFilename);
    cout << "\nSaved graph to: " << fullFilename << endl;
}
 
/* ************************************************************************* */
int main() {
    string factor_loc = findExampleDataFile("issue1452.txt");
    ifstream factor_file(factor_loc.c_str());
    cout << "Reading factors data file: " << factor_loc << endl;
 
    // Configure ISAM2 parameters for the fixed-lag smoother
    ISAM2Params isamParameters;
    isamParameters.relinearizeThreshold = 0.1;
    isamParameters.relinearizeSkip = 1;
 
    // Important!!!!!! Key parameter to ensure old factors are released after marginalization
    isamParameters.findUnusedFactorSlots = true;
    // Initialize fixed-lag smoother with a 1-second lag window
    const double lag = 1.0;
    IncrementalFixedLagSmoother smoother(lag, isamParameters);
    // Print the iSAM2 parameters (optional)
    isamParameters.print();
 
    // Containers for incremental updates
    NonlinearFactorGraph newFactors;
    Values newValues;
    FixedLagSmoother::KeyTimestampMap newTimestamps;
    // For tracking the latest estimate of all states in the sliding window
    Values currentEstimate;
    Pose3 lastPose;
 
    // Read and process each line in the factor graph file
    string line;
    int lineCount = 0;
    while (getline(factor_file, line)) {
        if (line.empty()) continue;  // Skip empty lines
        cout << "\n======================== Processing line " << ++lineCount
             << " =========================" << endl;
 
        istringstream iss(line);
        int factorType;
        iss >> factorType;
        // Check if the factor is PRIOR or BETWEEN
        if (factorType == PRIOR) {
            double timestamp;
            int key;
            double x, y, z, roll, pitch, yaw;
            iss >> timestamp >> key >> x >> y >> z >> roll >> pitch >> yaw;
            Pose3 pose = createPose(x, y, z, roll, pitch, yaw);
            Matrix6 cov = readCovarianceMatrix(iss);
            auto noise = noiseModel::Gaussian::Covariance(cov);
            // Add prior factor
            newFactors.addPrior(X(key), pose, noise);
            cout << "Add PRIOR factor on key " << key << endl;
            // Provide initial guess if not already in the graph
            if (!newValues.exists(X(key))) {
                newValues.insert(X(key), pose);
                newTimestamps[X(key)] = timestamp;
            }
        } else if (factorType == BETWEEN) {
            double timestamp;
            int key1, key2;
            iss >> timestamp >> key1 >> key2;
            double x1, y1, z1, roll1, pitch1, yaw1;
            iss >> x1 >> y1 >> z1 >> roll1 >> pitch1 >> yaw1;
            Pose3 relativePose = createPose(x1, y1, z1, roll1, pitch1, yaw1);
            Matrix6 cov = readCovarianceMatrix(iss);
            auto noise = noiseModel::Gaussian::Covariance(cov);
            // Add between factor
            newFactors.emplace_shared<BetweenFactor<Pose3>>(X(key1), X(key2), relativePose, noise);
            cout << "Add BETWEEN factor: " << key1 << " -> " << key2 << endl;
            // Provide an initial guess if needed
            if (!newValues.exists(X(key2))) {
                newValues.insert(X(key2), lastPose.compose(relativePose));
                newTimestamps[X(key2)] = timestamp;
            }
        } else {
            cerr << "Unknown factor type: " << factorType << endl;
            continue;
        }
 
        // Print some intermediate statistics
        cout << "Before update - Graph has " << smoother.getFactors().size()
             << " factors, " << smoother.getFactors().nrFactors() << " nr factors." << endl;
        cout << "New factors: " << newFactors.size()
             << ", New values: " << newValues.size() << endl;
 
        // Attempt to update the smoother with new factors and values
        try {
            smoother.update(newFactors, newValues, newTimestamps);
            // Optional: Perform extra internal iterations if needed
            size_t maxExtraIterations = 3;
            for (size_t i = 1; i < maxExtraIterations; ++i) {
                smoother.update();
            }
            // you may not get expected results if you use the gtsam version lower than 4.3
            cout << "After update - Graph has " << smoother.getFactors().size()
                 << " factors, " << smoother.getFactors().nrFactors() << " nr factors." << endl;
 
            // Retrieve the latest estimate
            currentEstimate = smoother.calculateEstimate();
            if (!currentEstimate.empty()) {
                // Update lastPose to the last key's estimate
                Key lastKey = currentEstimate.keys().back();
                lastPose = currentEstimate.at<Pose3>(lastKey);
            }
 
            // Clear containers for the next iteration
            newFactors.resize(0);
            newValues.clear();
            newTimestamps.clear();
        } catch (const exception &e) {
            cerr << "Smoother update failed: " << e.what() << endl;
        }
    }
 
    // The results of the last sliding window are saved to a g2o file for visualization or further analysis.
    saveG2oGraph(smoother.getFactors(), currentEstimate, "isam", lineCount);
    factor_file.close();
 
    return 0;
}