apex-solver 1.4.0

// GTSAM Bundle Adjustment Benchmark using native GTSAM BAL reader
// Uses SfmData and GeneralSFMFactor for proper BAL camera model handling

#include <gtsam/geometry/Point3.h>
#include <gtsam/geometry/Cal3Bundler.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/sfm/SfmData.h>
#include <gtsam/slam/GeneralSFMFactor.h>
#include <gtsam/inference/Symbol.h>

#include "../../common/include/ba_benchmark_utils.h"
#include "../include/gtsam_ba.h"


// Type aliases and using declarations moved to gtsam_ba.h

benchmark_utils::BenchmarkResult BenchmarkGTSAM(const std::string& dataset_path) {
    using namespace benchmark_utils;
    BenchmarkResult result;
    result.dataset = "problem-1723-156502-pre";
    result.solver = "GTSAM";
    result.language = "C++";

    // Use GTSAM's native BAL reader which handles camera model conventions correctly
    SfmData sfm_data;

    try {
        sfm_data = SfmData::FromBalFile(dataset_path);
    } catch (const std::exception& e) {
        std::cerr << "Failed to load BAL file: " << e.what() << std::endl;
        result.status = "LOAD_FAILED";
        return result;
    }

    result.num_cameras = sfm_data.numberCameras();
    result.num_points = sfm_data.numberTracks();

    // Count total observations
    size_t total_obs = 0;
    for (size_t j = 0; j < sfm_data.numberTracks(); ++j) {
        total_obs += sfm_data.tracks[j].numberMeasurements();
    }
    result.num_observations = static_cast<int>(total_obs);

    // Build factor graph using GeneralSFMFactor (optimizes full camera including calibration)
    NonlinearFactorGraph graph;
    Values initial;

    // Add camera variables
    for (size_t i = 0; i < sfm_data.numberCameras(); ++i) {
        initial.insert(C(i), sfm_data.cameras[i]);
    }

    // Add point variables and projection factors
    // Measurement noise with Huber robust loss
    auto noise = noiseModel::Robust::Create(
        noiseModel::mEstimator::Huber::Create(1.0),
        noiseModel::Isotropic::Sigma(2, 1.0));

    // Define the factor type
    using SfmFactor = GeneralSFMFactor<SfmCamera, Point3>;

    for (size_t j = 0; j < sfm_data.numberTracks(); ++j) {
        const SfmTrack& track = sfm_data.tracks[j];

        // Add 3D point
        initial.insert(P(j), track.point3());

        // Add projection factors for each measurement
        for (size_t k = 0; k < track.numberMeasurements(); ++k) {
            const auto& measurement = track.measurements[k];
            size_t camera_idx = measurement.first;
            Point2 uv = measurement.second;

            graph.emplace_shared<SfmFactor>(uv, noise, C(camera_idx), P(j));
        }
    }

    // Fix first camera for gauge freedom
    // Use a strong prior on the first camera (all 9 DOF: 6 pose + 3 calibration)
    auto prior_noise = noiseModel::Diagonal::Sigmas(
        (Vector(9) << 1e-6, 1e-6, 1e-6, 1e-6, 1e-6, 1e-6, 1e-6, 1e-6, 1e-6).finished());
    graph.addPrior(C(0), sfm_data.cameras[0], prior_noise);

    // Also add prior on first point to fix scale
    auto point_prior_noise = noiseModel::Isotropic::Sigma(3, 1e-6);
    graph.addPrior(P(0), sfm_data.tracks[0].point3(), point_prior_noise);

    // Configure optimizer
    // Use more conservative settings to handle potential numerical issues
    LevenbergMarquardtParams params;
    params.setVerbosityLM("SILENT");
    params.setMaxIterations(100);
    params.setAbsoluteErrorTol(1e-6);
    params.setRelativeErrorTol(1e-6);
    // Start with higher damping to take smaller, safer steps
    params.setlambdaInitial(1.0);
    params.setlambdaFactor(2.0);  // More gradual lambda changes
    params.setlambdaUpperBound(1e10);
    params.setlambdaLowerBound(1e-10);
    // Use diagonal damping for better conditioning
    params.setDiagonalDamping(true);

    // Compute initial error
    double initial_error = graph.error(initial);
    result.initial_mse = initial_error / result.num_observations;
    result.initial_rmse = std::sqrt(result.initial_mse);

    // Optimize
    Timer timer;

    try {
        LevenbergMarquardtOptimizer optimizer(graph, initial, params);
        Values optimized = optimizer.optimize();
        result.time_ms = timer.elapsed_ms();
        result.iterations = optimizer.iterations();

        // Compute final error
        double final_error = graph.error(optimized);
        result.final_mse = final_error / result.num_observations;
        result.final_rmse = std::sqrt(result.final_mse);

        result.status = "CONVERGED";
    } catch (const std::exception& e) {
        std::cerr << "Optimization failed: " << e.what() << std::endl;
        result.time_ms = timer.elapsed_ms();
        result.status = "FAILED";
        result.final_mse = result.initial_mse;
        result.final_rmse = result.initial_rmse;
    }

    return result;
}

int main(int argc, char** argv) {
    std::string dataset_path = "../../../data/bundle_adjustment/ladybug/problem-1723-156502-pre.txt";

    if (argc > 1) {
        dataset_path = argv[1];
    }

    std::vector<benchmark_utils::BenchmarkResult> results;
    results.push_back(BenchmarkGTSAM(dataset_path));

    std::string csv_path = "gtsam_ba_benchmark_results.csv";
    if (!benchmark_utils::WriteResultsToCSV(csv_path, results)) {
        std::cerr << "Failed to write CSV results" << std::endl;
        return 1;
    }

    return 0;
}