mirror of
https://github.com/introlab/rtabmap.git
synced 2026-10-05 17:47:49 +08:00
66 lines
3.0 KiB
C++
66 lines
3.0 KiB
C++
/*
|
|
Copyright (c) 2010-2026, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
|
|
All rights reserved.
|
|
|
|
Redistribution and use in source and binary forms, with or without
|
|
modification, are permitted provided that the following conditions are met:
|
|
* Redistributions of source code must retain the above copyright
|
|
notice, this list of conditions and the following disclaimer.
|
|
* Redistributions in binary form must reproduce the above copyright
|
|
notice, this list of conditions and the following disclaimer in the
|
|
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.
|
|
|
|
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
|
|
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
|
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
|
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
|
|
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
|
|
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
|
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
|
|
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
|
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
|
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*/
|
|
|
|
#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_ */
|