CICP.h

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2018, Individual contributors, see AUTHORS file     |
   | See: http://www.mrpt.org/Authors - All rights reserved.                |
   | Released under BSD License. See details in http://www.mrpt.org/License |
   +------------------------------------------------------------------------+ */
#ifndef CICP_H
#define CICP_H
 
#include <mrpt/slam/CMetricMapsAlignmentAlgorithm.h>
#include <mrpt/config/CLoadableOptions.h>
#include <mrpt/typemeta/TEnumType.h>
 
namespace mrpt
{
namespace slam
{
/** The ICP algorithm selection, used in mrpt::slam::CICP::options  \ingroup
 * mrpt_slam_grp  */
enum TICPAlgorithm
{
        icpClassic = 0,
        icpLevenbergMarquardt
};
 
/** ICP covariance estimation methods, used in mrpt::slam::CICP::options
 * \ingroup mrpt_slam_grp  */
enum TICPCovarianceMethod
{
        /** Use the covariance of the optimal registration, disregarding uncertainty
           in data association */
        icpCovLinealMSE = 0,
        /** Covariance of a least-squares optimizer (includes data association
           uncertainty) */
        icpCovFiniteDifferences
};
 
/** Several implementations of ICP (Iterative closest point) algorithms for
 * aligning two point maps or a point map wrt a grid map.
 *
 *  CICP::AlignPDF() or CICP::Align() are the two main entry points of the
 * algorithm.
 *
 *  To choose among existing ICP algorithms or customizing their parameters, see
 * CICP::TConfigParams and the member \a options.
 *
 *  There exists an extension of the original ICP algorithm that provides
 * multihypotheses-support for the correspondences, and which generates a
 * Sum-of-Gaussians (SOG)
 *    PDF as output. See mrpt::tfest::se2_l2_robust()
 *
 * For further details on the implemented methods, check the web:
 *   http://www.mrpt.org/Iterative_Closest_Point_(ICP)_and_other_matching_algorithms
 *
 *  For a paper explaining some of the basic equations, see for example:
 *   J. Martinez, J. Gonzalez, J. Morales, A. Mandow, A. Garcia-Cerezo,
 *   "Mobile robot motion estimation by 2D scan matching with genetic and
 * iterative closest point algorithms",
 *    Journal of Field Robotics, vol. 23, no. 1, pp. 21-34, 2006. (
 * http://babel.isa.uma.es/~jlblanco/papers/martinez2006gil.pdf )
 *
 * \sa CMetricMapsAlignmentAlgorithm
 * \ingroup mrpt_slam_grp
 */
class CICP : public mrpt::slam::CMetricMapsAlignmentAlgorithm
{
   public:
        /** The ICP algorithm configuration data
         */
        class TConfigParams : public mrpt::config::CLoadableOptions
        {
           public:
                void loadFromConfigFile(
                        const mrpt::config::CConfigFileBase& source,
                        const std::string& section) override;  // See base docs
                void saveToConfigFile(
                        mrpt::config::CConfigFileBase& c, const std::string& s) const override;
 
                /** @name Algorithms selection
                        @{ */
                /** The algorithm to use (default: icpClassic). See
                 * http://www.mrpt.org/tutorials/programming/scan-matching-and-icp/ for
                 * details */
                TICPAlgorithm ICP_algorithm{icpClassic};
                /** The method to use for covariance estimation (Default:
                 * icpCovFiniteDifferences) */
                TICPCovarianceMethod ICP_covariance_method{icpCovFiniteDifferences};
                /** @} */
 
                /** @name Correspondence-finding criteria
                        @{ */
                bool onlyUniqueRobust{false};
                //! if this option is enabled only the closest
                //! correspondence for each reference point will
                //! be kept (default=false).
                /** @} */
 
                /** @name Termination criteria
                        @{ */
                /** Maximum number of iterations to run. */
                unsigned int maxIterations{40};
                /** If the correction in all translation coordinates (X,Y,Z) is below
                 * this threshold (in meters), iterations are terminated (Default:1e-6)
                 */
                double minAbsStep_trans{1e-6};
                /** If the correction in all rotation coordinates (yaw,pitch,roll) is
                 * below this threshold (in radians), iterations are terminated
                 * (Default:1e-6) */
                double minAbsStep_rot{1e-6};
                /** @} */
 
                /** Initial threshold distance for two points to become a
                 * correspondence. */
                double thresholdDist{0.75}, thresholdAng{0.15 * M_PI / 180.0};
                /** The scale factor for thresholds everytime convergence is achieved.
                 */
                double ALFA{0.5};
                /** The size for threshold such that iterations will stop, since it is
                 * considered precise enough. */
                double smallestThresholdDist{0.1};
 
                /** This is the normalization constant \f$ \sigma^2_p \f$ that is used
                 * to scale the whole 3x3 covariance.
                 *  This has a default value of \f$ (0.02)^2 \f$, that is, a 2cm sigma.
                 *  See paper: J.L. Blanco, J. Gonzalez-Jimenez, J.A.
                 * Fernandez-Madrigal, "A Robust, Multi-Hypothesis Approach to Matching
                 * Occupancy Grid Maps", Robotica, vol. 31, no. 5, pp. 687-701, 2013.
                 */
                double covariance_varPoints{0.02 * 0.02};
 
                /** Perform a RANSAC step, mrpt::tfest::se2_l2_robust(), after the ICP
                 * convergence, to obtain a better estimation of the pose PDF. */
                bool doRANSAC{false};
 
                /** @name RANSAC-step options for mrpt::tfest::se2_l2_robust() if \a
                 * doRANSAC=true
                 * @{ */
                unsigned int ransac_minSetSize{3}, ransac_maxSetSize{20},
                        ransac_nSimulations{100};
                double ransac_mahalanobisDistanceThreshold{3.0};
                /** RANSAC-step option: The standard deviation in X,Y of
                 * landmarks/points which are being matched (used to compute covariances
                 * in the SoG) */
                double normalizationStd{0.02};
                bool ransac_fuseByCorrsMatch{true};
                double ransac_fuseMaxDiffXY{0.01},
                        ransac_fuseMaxDiffPhi{0.1 * M_PI / 180.0};
                /** @} */
 
                /** Cauchy kernel rho, for estimating the optimal transformation
                 * covariance, in meters (default = 0.07m) */
                double kernel_rho{0.07};
                /** Whether to use kernel_rho to smooth distances, or use distances
                 * directly (default=true) */
                bool use_kernel{true};
                /** [LM method only] The size of the perturbance in x & y used to
                 * estimate the Jacobians of the square error (default=0.05) */
                double Axy_aprox_derivatives{0.05};
                /** [LM method only] The initial value of the lambda parameter in the LM
                 * method (default=1e-4) */
                double LM_initial_lambda{1e-4};
 
                /** Skip the computation of the covariance (saves some time)
                 * (default=false) */
                bool skip_cov_calculation{false};
                /** Skip the (sometimes) expensive evaluation of the term 'quality' at
                 * ICP output (Default=true) */
                bool skip_quality_calculation{true};
 
                /** Decimation of the point cloud being registered against the reference
                 * one (default=5) - set to 1 to have the older (MRPT <0.9.5) behavior
                 *  of not approximating ICP by ignoring the correspondence of some
                 * points. The speed-up comes from a decimation of the number of KD-tree
                 * queries,
                 *  the most expensive step in ICP */
                uint32_t corresponding_points_decimation{5};
        };
 
        /** The options employed by the ICP align. */
        TConfigParams options;
 
        /** Constructor with the default options */
        CICP() : options() {}
        /** Constructor that directly set the ICP params from a given struct \sa
         * options */
        CICP(const TConfigParams& icpParams) : options(icpParams) {}
        /** Destructor */
        virtual ~CICP() {}
        /** The ICP algorithm return information*/
        struct TReturnInfo
        {
                TReturnInfo() : nIterations(0), goodness(0), quality(0) {}
                /** The number of executed iterations until convergence */
                unsigned short nIterations;
                /** A goodness measure for the alignment, it is a [0,1] range indicator
                 * of percentage of correspondences. */
                float goodness;
                /** A measure of the 'quality' of the local minimum of the sqr. error
                 * found by the method. Higher values are better. Low values will be
                 * found in ill-conditioned situations (e.g. a corridor) */
                float quality;
        };
 
        /** An implementation of CMetricMapsAlignmentAlgorithm for the case of a
         * point maps and a occupancy grid/point map.
         *
         *  This method computes the PDF of the displacement (relative pose) between
         *   two maps: <b>the relative pose of m2 with respect to m1</b>. This pose
         *   is returned as a PDF rather than a single value.
         *
         *  \note This method can be configurated with "CICP::options"
         *  \note The output PDF is a CPosePDFGaussian if "doRANSAC=false", or a
         * CPosePDFSOG otherwise.
         *
         * \param m1                    [IN] The first map (CAN BE A mrpt::poses::CPointsMap
         * derived
         * class or a mrpt::slam::COccupancyGrid2D class)
         * \param m2                    [IN] The second map. (MUST BE A
         * mrpt::poses::CPointsMap
         * derived class)The pose of this map respect to m1 is to be estimated.
         * \param initialEstimationPDF  [IN] An initial gross estimation for the
         * displacement.
         * \param runningTime   [OUT] A pointer to a container for obtaining the
         * algorithm running time in seconds, or nullptr if you don't need it.
         * \param info                  [OUT] A pointer to a CICP::TReturnInfo, or nullptr
         * if
         * it
         * isn't needed.
         *
         * \return A smart pointer to the output estimated pose PDF.
         *
         * \sa CMetricMapsAlignmentAlgorithm, CICP::options, CICP::TReturnInfo
         */
        mrpt::poses::CPosePDF::Ptr AlignPDF(
                const mrpt::maps::CMetricMap* m1, const mrpt::maps::CMetricMap* m2,
                const mrpt::poses::CPosePDFGaussian& initialEstimationPDF,
                float* runningTime = nullptr, void* info = nullptr);
 
        // See base class for docs
        mrpt::poses::CPose3DPDF::Ptr Align3DPDF(
                const mrpt::maps::CMetricMap* m1, const mrpt::maps::CMetricMap* m2,
                const mrpt::poses::CPose3DPDFGaussian& initialEstimationPDF,
                float* runningTime = nullptr, void* info = nullptr);
 
   protected:
        /** Computes:
         *  \f[ K(x^2) = \frac{x^2}{x^2+\rho^2}  \f]
         *  or just return the input if options.useKernel = false.
         */
        float kernel(const float& x2, const float& rho2);
 
        mrpt::poses::CPosePDF::Ptr ICP_Method_Classic(
                const mrpt::maps::CMetricMap* m1, const mrpt::maps::CMetricMap* m2,
                const mrpt::poses::CPosePDFGaussian& initialEstimationPDF,
                TReturnInfo& outInfo);
        mrpt::poses::CPosePDF::Ptr ICP_Method_LM(
                const mrpt::maps::CMetricMap* m1, const mrpt::maps::CMetricMap* m2,
                const mrpt::poses::CPosePDFGaussian& initialEstimationPDF,
                TReturnInfo& outInfo);
        mrpt::poses::CPose3DPDF::Ptr ICP3D_Method_Classic(
                const mrpt::maps::CMetricMap* m1, const mrpt::maps::CMetricMap* m2,
                const mrpt::poses::CPose3DPDFGaussian& initialEstimationPDF,
                TReturnInfo& outInfo);
};
}  // namespace slam
}  // namespace mrpt
 
MRPT_ENUM_TYPE_BEGIN(mrpt::slam::TICPAlgorithm)
using namespace mrpt::slam;
MRPT_FILL_ENUM(icpClassic);
MRPT_FILL_ENUM(icpLevenbergMarquardt);
MRPT_ENUM_TYPE_END()
 
MRPT_ENUM_TYPE_BEGIN(mrpt::slam::TICPCovarianceMethod)
using namespace mrpt::slam;
MRPT_FILL_ENUM(icpCovLinealMSE);
MRPT_FILL_ENUM(icpCovFiniteDifferences);
MRPT_ENUM_TYPE_END()
 
#endif