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 
     
       voidpcl::SACSegmentation
      
       ::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
       
         (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
        
          (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
         
           (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
          
            (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
           
             (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
            
              (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
             
               (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_); }}