[PCL]模型拟合方法——随机采样一致性

SACSegmentation封装了多种Ransac方法，包括：

RandomSampleConsensus，

LeastMedianSquares，

MEstimatorSampleConsensus

ProgressiveSampleConsensus，

RandomizedRandomSampleConsensus，

RandomizedMEstimatorSampleConsensus，

MaximumLikelihoodSampleConsensus

1.PCL所谓的平行线判断，是已知一个法向量，判断面与之平行。
2.PCL直线Ransac拟合，为啥只需要设置一个距离阈值？因为默认值迭代50次
 

template <typename PointT> void

pcl::SACSegmentation<PointT>::initSAC (const int method_type)

{

  if (sac_)

    sac_.reset ();

  // Build the sample consensus method

  switch (method_type)

  {

    case SAC_RANSAC:

    default:

    {

      PCL_DEBUG ("[pcl::%s::initSAC] Using a method of type: SAC_RANSAC with a model threshold of %f\n", getClassName ().c_str (), threshold_);

      sac_.reset (new RandomSampleConsensus<PointT> (model_, threshold_));

      break;

    }

    case SAC_LMEDS:

    {

      PCL_DEBUG ("[pcl::%s::initSAC] Using a method of type: SAC_LMEDS with a model threshold of %f\n", getClassName ().c_str (), threshold_);

      sac_.reset (new LeastMedianSquares<PointT> (model_, threshold_));

      break;

    }

    case SAC_MSAC:

    {

      PCL_DEBUG ("[pcl::%s::initSAC] Using a method of type: SAC_MSAC with a model threshold of %f\n", getClassName ().c_str (), threshold_);

      sac_.reset (new MEstimatorSampleConsensus<PointT> (model_, threshold_));

      break;

    }

    case SAC_RRANSAC:

    {

      PCL_DEBUG ("[pcl::%s::initSAC] Using a method of type: SAC_RRANSAC with a model threshold of %f\n", getClassName ().c_str (), threshold_);

      sac_.reset (new RandomizedRandomSampleConsensus<PointT> (model_, threshold_));

      break;

    }

    case SAC_RMSAC:

    {

      PCL_DEBUG ("[pcl::%s::initSAC] Using a method of type: SAC_RMSAC with a model threshold of %f\n", getClassName ().c_str (), threshold_);

      sac_.reset (new RandomizedMEstimatorSampleConsensus<PointT> (model_, threshold_));

      break;

    }

    case SAC_MLESAC:

    {

      PCL_DEBUG ("[pcl::%s::initSAC] Using a method of type: SAC_MLESAC with a model threshold of %f\n", getClassName ().c_str (), threshold_);

      sac_.reset (new MaximumLikelihoodSampleConsensus<PointT> (model_, threshold_));

      break;

    }

    case SAC_PROSAC:

    {

      PCL_DEBUG ("[pcl::%s::initSAC] Using a method of type: SAC_PROSAC with a model threshold of %f\n", getClassName ().c_str (), threshold_);

      sac_.reset (new ProgressiveSampleConsensus<PointT> (model_, threshold_));

      break;

    }

  }

  // Set the Sample Consensus parameters if they are given/changed

  if (sac_->getProbability () != probability_)

  {

    PCL_DEBUG ("[pcl::%s::initSAC] Setting the desired probability to %f\n", getClassName ().c_str (), probability_);

    sac_->setProbability (probability_);

  }

  if (max_iterations_ != -1 && sac_->getMaxIterations () != max_iterations_)

  {

    PCL_DEBUG ("[pcl::%s::initSAC] Setting the maximum number of iterations to %d\n", getClassName ().c_str (), max_iterations_);

    sac_->setMaxIterations (max_iterations_);

  }

  if (samples_radius_ > 0.)

  {

    PCL_DEBUG ("[pcl::%s::initSAC] Setting the maximum sample radius to %f\n", getClassName ().c_str (), samples_radius_);

    // Set maximum distance for radius search during random sampling

    model_->setSamplesMaxDist (samples_radius_, samples_radius_search_);

  }

}

　　

[PCL]模型拟合方法——随机采样一致性的相关教程结束。