/*
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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      documentation and/or other materials provided with the distribution.
    * 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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*/

#ifndef LIDARCAMERACALIBRATION_G2OSOLVER_H_
#define LIDARCAMERACALIBRATION_G2OSOLVER_H_

#include "CorrectionSolver.h"
#include "PatternSearchSolver.h"

#include <rtabmap/core/Version.h>

#ifdef RTABMAP_G2O

// Least squares with g2o: Levenberg-Marquardt on the distances of the lidar edge points
// to the images' edges (chamfer matching), weighted by the points' weights.
//
// - A redescending robust kernel (Welsch) makes a point far from any image edge count for
//   nothing, as the score does: many lidar edges have no counterpart in the image
//   (speckle, surfaces the camera does not see the same way).
// - Levenberg-Marquardt only follows the local slope, and each point is pulled toward its
//   nearest image edge, often not its own when far from the solution: from a few degrees
//   away, it stops in a local minimum. So a coarse pattern search gets close first, then
//   the kernel's scale goes from wide to narrow (9, 3, then 1 x sigma).
class G2oSolver : public CorrectionSolver
{
public:
	G2oSolver(double sigma) : sigma_(sigma), coarse_(4) {}
	const char * name() const override {return "g2o";}
	void solve(const CalibrationProblem & problem, bool estimateTranslation, double p[6]) const override;

private:
	template<int D>
	void solveWith(const CalibrationProblem & problem, const std::vector<int> & solved, double p[6]) const;

	double sigma_;
	PatternSearchSolver coarse_;  // steps of 2 down to 0.25 deg
};

#endif

#endif /* LIDARCAMERACALIBRATION_G2OSOLVER_H_ */