rtabmap/tools/LidarCameraCalibration/GtsamSolver.cpp

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

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      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