CICP.h

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/* +---------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)               |
   |                          http://www.mrpt.org/                             |
   |                                                                           |
   | Copyright (c) 2005-2017, 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/utils/CLoadableOptions.h>
#include <mrpt/utils/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 {
                        icpCovLinealMSE = 0,         //!< Use the covariance of the optimal registration, disregarding uncertainty in data association
                        icpCovFiniteDifferences      //!< Covariance of a least-squares optimizer (includes data association uncertainty)
                };
 
                /** 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 SLAM_IMPEXP  CICP : public mrpt::slam::CMetricMapsAlignmentAlgorithm
                {
                public:
                        /** The ICP algorithm configuration data
                         */
                        class SLAM_IMPEXP TConfigParams : public utils::CLoadableOptions
                        {
                        public:
                                TConfigParams();        //!< Initializer for default values:
 
                                void loadFromConfigFile(const mrpt::utils::CConfigFileBase &source,const std::string &section) MRPT_OVERRIDE; // See base docs
                                void dumpToTextStream(mrpt::utils::CStream &out) const MRPT_OVERRIDE; // See base docs
 
                                /** @name Algorithms selection
                                    @{ */
                                TICPAlgorithm         ICP_algorithm; //!< The algorithm to use (default: icpClassic). See http://www.mrpt.org/tutorials/programming/scan-matching-and-icp/ for details
                                TICPCovarianceMethod  ICP_covariance_method; //!< The method to use for covariance estimation (Default: icpCovFiniteDifferences)
                                /** @} */
 
                                /** @name Correspondence-finding criteria
                                    @{ */
                                bool  onlyClosestCorrespondences;  //!< The usual approach: to consider only the closest correspondence for each local point (Default to true)
                                bool  onlyUniqueRobust;            //! Apart of "onlyClosestCorrespondences=true", if this option is enabled only the closest correspondence for each reference point will be kept (default=false).
                                /** @} */
 
                                /** @name Termination criteria
                                    @{ */
                                unsigned int    maxIterations;  //!< Maximum number of iterations to run.
                                float           minAbsStep_trans; //!< If the correction in all translation coordinates (X,Y,Z) is below this threshold (in meters), iterations are terminated (Default:1e-6)
                                float           minAbsStep_rot;   //!< If the correction in all rotation coordinates (yaw,pitch,roll) is below this threshold (in radians), iterations are terminated (Default:1e-6)
                                /** @} */
 
                                float   thresholdDist,thresholdAng; //!< Initial threshold distance for two points to become a correspondence.
                                float   ALFA;  //!< The scale factor for threshold everytime convergence is achieved.
                                float   smallestThresholdDist;  //!< The size for threshold such that iterations will stop, since it is considered precise enough.
 
                                /** 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.
                                  */
                                float   covariance_varPoints;
 
                                bool    doRANSAC;  //!< Perform a RANSAC step, mrpt::tfest::se2_l2_robust(), after the ICP convergence, to obtain a better estimation of the pose PDF.
 
                                /** @name RANSAC-step options for mrpt::tfest::se2_l2_robust() if \a doRANSAC=true
                                  * @{ */
                                unsigned int ransac_minSetSize,ransac_maxSetSize,ransac_nSimulations;
                                float        ransac_mahalanobisDistanceThreshold;
                                float        normalizationStd; //!< RANSAC-step option: The standard deviation in X,Y of landmarks/points which are being matched (used to compute covariances in the SoG)
                                bool         ransac_fuseByCorrsMatch;
                                float        ransac_fuseMaxDiffXY, ransac_fuseMaxDiffPhi;
                                /** @} */
 
                                float   kernel_rho; //!< Cauchy kernel rho, for estimating the optimal transformation covariance, in meters (default = 0.07m)
                                bool    use_kernel; //!< Whether to use kernel_rho to smooth distances, or use distances directly (default=true)
                                float   Axy_aprox_derivatives; //!< [LM method only] The size of the perturbance in x & y used to estimate the Jacobians of the square error (default=0.05)
                                float   LM_initial_lambda; //!< [LM method only] The initial value of the lambda parameter in the LM method (default=1e-4)
 
                                bool    skip_cov_calculation; //!< Skip the computation of the covariance (saves some time) (default=false)
                                bool    skip_quality_calculation;  //!< Skip the (sometimes) expensive evaluation of the term 'quality' at ICP output (Default=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;
                        };
 
                        TConfigParams  options; //!< The options employed by the ICP align.
 
                        /** 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) { }
                        virtual ~CICP() { }     //!< Destructor
 
                        /** The ICP algorithm return information*/
                        struct SLAM_IMPEXP TReturnInfo
                        {
                                TReturnInfo() :
                                        nIterations(0),
                                        goodness(0),
                                        quality(0)
                                {
                                }
                                unsigned short nIterations; //!< The number of executed iterations until convergence
                                float          goodness;    //!< A goodness measure for the alignment, it is a [0,1] range indicator of percentage of correspondences.
                                float          quality;     //!< 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)
                        };
 
                        /** 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 NULL if you don't need it.
                         * \param info                  [OUT] A pointer to a CICP::TReturnInfo, or NULL if it isn't needed.
                         *
                         * \return A smart pointer to the output estimated pose PDF.
                         *
                         * \sa CMetricMapsAlignmentAlgorithm, CICP::options, CICP::TReturnInfo
                         */
                        mrpt::poses::CPosePDFPtr AlignPDF(
                                        const mrpt::maps::CMetricMap            *m1,
                                        const mrpt::maps::CMetricMap            *m2,
                                        const mrpt::poses::CPosePDFGaussian     &initialEstimationPDF,
                                        float                                   *runningTime = NULL,
                                        void                                    *info = NULL );
 
                        // See base class for docs
                        mrpt::poses::CPose3DPDFPtr Align3DPDF(
                                        const mrpt::maps::CMetricMap            *m1,
                                        const mrpt::maps::CMetricMap            *m2,
                                        const mrpt::poses::CPose3DPDFGaussian   &initialEstimationPDF,
                                        float                                   *runningTime = NULL,
                                        void                                    *info = NULL );
 
 
                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::CPosePDFPtr ICP_Method_Classic(
                                const mrpt::maps::CMetricMap            *m1,
                                const mrpt::maps::CMetricMap            *m2,
                                const mrpt::poses::CPosePDFGaussian     &initialEstimationPDF,
                                TReturnInfo                             &outInfo );
                        mrpt::poses::CPosePDFPtr ICP_Method_LM(
                                const mrpt::maps::CMetricMap            *m1,
                                const mrpt::maps::CMetricMap            *m2,
                                const mrpt::poses::CPosePDFGaussian     &initialEstimationPDF,
                                TReturnInfo                             &outInfo );
                        mrpt::poses::CPose3DPDFPtr ICP3D_Method_Classic(
                                const mrpt::maps::CMetricMap            *m1,
                                const mrpt::maps::CMetricMap            *m2,
                                const mrpt::poses::CPose3DPDFGaussian &initialEstimationPDF,
                                TReturnInfo                             &outInfo );
                };
        } // End of namespace
 
        // Specializations MUST occur at the same namespace:
        namespace utils
        {
                template <>
                struct TEnumTypeFiller<slam::TICPAlgorithm>
                {
                        typedef slam::TICPAlgorithm enum_t;
                        static void fill(bimap<enum_t,std::string>  &m_map)
                        {
                                m_map.insert(slam::icpClassic, "icpClassic");
                                m_map.insert(slam::icpLevenbergMarquardt, "icpLevenbergMarquardt");
                        }
                };
                template <>
                struct TEnumTypeFiller<slam::TICPCovarianceMethod>
                {
                        typedef slam::TICPCovarianceMethod enum_t;
                        static void fill(bimap<enum_t,std::string>  &m_map)
                        {
                                m_map.insert(slam::icpCovLinealMSE, "icpCovLinealMSE");
                                m_map.insert(slam::icpCovFiniteDifferences, "icpCovFiniteDifferences");
                        }
                };
        } // End of namespace
} // End of namespace
 
#endif