/*
Copyright (c) 2010-2026, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.

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    * Redistributions in binary form must reproduce the above copyright
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    * Neither the name of the Universite de Sherbrooke nor the
      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.

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*/

#include "GtsamSolver.h"

#ifdef RTABMAP_GTSAM

#include <gtsam/linear/NoiseModel.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>

#include <algorithm>
#include <cmath>

namespace {

// One lidar edge point: CalibrationProblem::edgeDistance() for it, as a function of the
// solved parameters x of p.
template<int D>
class EdgeDistanceFactor : public gtsam::NoiseModelFactor1<Eigen::Matrix<double, D, 1> >
{
	typedef Eigen::Matrix<double, D, 1> X;
public:
	EdgeDistanceFactor(gtsam::Key key, const gtsam::SharedNoiseModel & model,
			const CalibrationProblem & problem, const Frame & frame, size_t point,
			const std::vector<int> & solved, const double p[6], double cap) :
		gtsam::NoiseModelFactor1<X>(model, key),
		problem_(problem), frame_(frame), point_(point), solved_(solved), cap_(cap)
	{
		std::copy(p, p + 6, p_);
	}

	gtsam::Vector evaluateError(const X & x,
#if GTSAM_VERSION_NUMERIC >= 40300
			gtsam::OptionalMatrixType H = OptionalNone) const override
#else
			boost::optional<gtsam::Matrix &> H = boost::none) const override
#endif
	{
		if(H)
		{
			// Central differences, with steps of the precision the projection needs.
			gtsam::Matrix J(1, D);
			for(int k = 0; k < D; ++k)
			{
				const double h = solved_[k] < 3 ? 1e-4 : 1e-3;  // m, deg
				X plus = x, minus = x;
				plus[k] += h;
				minus[k] -= h;
				J(0, k) = (residual(plus) - residual(minus)) / (2.0 * h);
			}
			*H = J;
		}
		return gtsam::Vector1(residual(x));
	}

private:
	double residual(const X & x) const
	{
		double q[6];
		std::copy(p_, p_ + 6, q);
		for(int k = 0; k < D; ++k)
		{
			q[solved_[k]] = x[k];
		}
		return problem_.edgeDistance(frame_, point_, q, cap_);
	}

	const CalibrationProblem & problem_;
	const Frame & frame_;
	size_t point_;
	std::vector<int> solved_;
	double p_[6];
	double cap_;
};

}  // namespace

void GtsamSolver::solve(const CalibrationProblem & problem, bool estimateTranslation, double p[6]) const
{
	coarse_.solve(problem, estimateTranslation, p);
	if(estimateTranslation)
	{
		solveWith<6>(problem, {0, 1, 2, 3, 4, 5}, p);
	}
	else
	{
		solveWith<3>(problem, {3, 4, 5}, p);
	}
}

template<int D>
void GtsamSolver::solveWith(const CalibrationProblem & problem, const std::vector<int> & solved, double p[6]) const
{
	typedef Eigen::Matrix<double, D, 1> X;
	const gtsam::Key key = 0;

	X x;
	for(int k = 0; k < D; ++k)
	{
		x[k] = p[solved[k]];
	}
	for(double scale : {9.0, 3.0, 1.0})
	{
		const double delta = scale * sigma_;
		const double cap = 10.0 * delta;  // beyond a few times the scale, a point does not count anyway
		const gtsam::noiseModel::mEstimator::Welsch::shared_ptr welsch =
				gtsam::noiseModel::mEstimator::Welsch::Create(delta);
		gtsam::NonlinearFactorGraph graph;
		for(int k : problem.nodes())
		{
			const Frame & f = problem.frames()[k];
			for(size_t i = 0; i < f.edgePoints.size(); ++i)
			{
				// Information = the point's weight, as with g2o.
				const gtsam::SharedNoiseModel model = gtsam::noiseModel::Robust::Create(
						welsch, gtsam::noiseModel::Isotropic::Sigma(1, 1.0 / std::sqrt(f.edgeWeights[i])));
				graph.emplace_shared<EdgeDistanceFactor<D> >(key, model, problem, f, i, solved, p, cap);
			}
		}
		gtsam::Values initial;
		initial.insert(key, x);
		gtsam::LevenbergMarquardtParams params;
		params.setMaxIterations(10);  // close already, after the coarse search
		x = gtsam::LevenbergMarquardtOptimizer(graph, initial, params).optimize().template at<X>(key);
	}
	for(int k = 0; k < D; ++k)
	{
		p[solved[k]] = x[k];
	}
}

#endif