163 lines
6.8 KiB
C++
163 lines
6.8 KiB
C++
/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2009, Willow Garage, Inc.
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* Copyright (c) 2012-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* $Id$
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*
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*/
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#pragma once
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#include <pcl/sample_consensus/sac.h>
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#include <pcl/sample_consensus/sac_model.h>
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#include <pcl/pcl_base.h>
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namespace pcl
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{
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/** \brief @b MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood
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* Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to
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* estimating image geometry", P.H.S. Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000.
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* \note MLESAC is useful in situations where most of the data samples belong to the model, and a fast outlier rejection algorithm is needed.
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* \author Radu B. Rusu
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* \ingroup sample_consensus
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*/
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template <typename PointT>
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class MaximumLikelihoodSampleConsensus : public SampleConsensus<PointT>
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{
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using SampleConsensusModelPtr = typename SampleConsensusModel<PointT>::Ptr;
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using PointCloudConstPtr = typename SampleConsensusModel<PointT>::PointCloudConstPtr;
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public:
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using Ptr = shared_ptr<MaximumLikelihoodSampleConsensus<PointT> >;
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using ConstPtr = shared_ptr<const MaximumLikelihoodSampleConsensus<PointT> >;
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using SampleConsensus<PointT>::max_iterations_;
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using SampleConsensus<PointT>::threshold_;
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using SampleConsensus<PointT>::iterations_;
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using SampleConsensus<PointT>::sac_model_;
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using SampleConsensus<PointT>::model_;
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using SampleConsensus<PointT>::model_coefficients_;
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using SampleConsensus<PointT>::inliers_;
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using SampleConsensus<PointT>::probability_;
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/** \brief MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor
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* \param[in] model a Sample Consensus model
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*/
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MaximumLikelihoodSampleConsensus (const SampleConsensusModelPtr &model) :
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SampleConsensus<PointT> (model),
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iterations_EM_ (3), // Max number of EM (Expectation Maximization) iterations
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sigma_ (0)
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{
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max_iterations_ = 10000; // Maximum number of trials before we give up.
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}
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/** \brief MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor
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* \param[in] model a Sample Consensus model
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* \param[in] threshold distance to model threshold
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*/
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MaximumLikelihoodSampleConsensus (const SampleConsensusModelPtr &model, double threshold) :
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SampleConsensus<PointT> (model, threshold),
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iterations_EM_ (3), // Max number of EM (Expectation Maximization) iterations
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sigma_ (0)
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{
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max_iterations_ = 10000; // Maximum number of trials before we give up.
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}
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/** \brief Compute the actual model and find the inliers
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* \param[in] debug_verbosity_level enable/disable on-screen debug information and set the verbosity level
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*/
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bool
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computeModel (int debug_verbosity_level = 0) override;
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/** \brief Set the number of EM iterations.
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* \param[in] iterations the number of EM iterations
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*/
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inline void
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setEMIterations (int iterations) { iterations_EM_ = iterations; }
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/** \brief Get the number of EM iterations. */
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inline int
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getEMIterations () const { return (iterations_EM_); }
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protected:
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/** \brief Compute the median absolute deviation:
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* \f[
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* MAD = \sigma * median_i (| Xi - median_j(Xj) |)
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* \f]
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* \note Sigma needs to be chosen carefully (a good starting sigma value is 1.4826)
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* \param[in] cloud the point cloud data message
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* \param[in] indices the set of point indices to use
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* \param[in] sigma the sigma value
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*/
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double
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computeMedianAbsoluteDeviation (const PointCloudConstPtr &cloud,
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const IndicesPtr &indices,
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double sigma) const;
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/** \brief Determine the minimum and maximum 3D bounding box coordinates for a given set of points
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* \param[in] cloud the point cloud message
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* \param[in] indices the set of point indices to use
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* \param[out] min_p the resultant minimum bounding box coordinates
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* \param[out] max_p the resultant maximum bounding box coordinates
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*/
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void
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getMinMax (const PointCloudConstPtr &cloud,
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const IndicesPtr &indices,
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Eigen::Vector4f &min_p,
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Eigen::Vector4f &max_p) const;
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/** \brief Compute the median value of a 3D point cloud using a given set point indices and return it as a Point32.
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* \param[in] cloud the point cloud data message
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* \param[in] indices the point indices
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* \param[out] median the resultant median value
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*/
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void
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computeMedian (const PointCloudConstPtr &cloud,
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const IndicesPtr &indices,
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Eigen::Vector4f &median) const;
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private:
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/** \brief Maximum number of EM (Expectation Maximization) iterations. */
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int iterations_EM_;
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/** \brief The MLESAC sigma parameter. */
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double sigma_;
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/sample_consensus/impl/mlesac.hpp>
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#endif
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