本文整理汇总了C++中eigen::Matrix4f::isApprox方法的典型用法代码示例。如果您正苦于以下问题：C++ Matrix4f::isApprox方法的具体用法？C++ Matrix4f::isApprox怎么用？C++ Matrix4f::isApprox使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类eigen::Matrix4f的用法示例。
在下文中一共展示了Matrix4f::isApprox方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: getClassName
template <typename PointSource, typename PointTarget, typename FeatureT> void
pcl::SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT>::computeTransformation (PointCloudSource &output, const Eigen::Matrix4f& guess)
{
if (!input_features_)
{
PCL_ERROR ("[pcl::%s::computeTransformation] ", getClassName ().c_str ());
PCL_ERROR ("No source features were given! Call setSourceFeatures before aligning.\n");
return;
}
if (!target_features_)
{
PCL_ERROR ("[pcl::%s::computeTransformation] ", getClassName ().c_str ());
PCL_ERROR ("No target features were given! Call setTargetFeatures before aligning.\n");
return;
}
if (!error_functor_)
{
error_functor_.reset (new TruncatedError (static_cast<float> (corr_dist_threshold_)));
}
std::vector<int> sample_indices (nr_samples_);
std::vector<int> corresponding_indices (nr_samples_);
PointCloudSource input_transformed;
float error, lowest_error (0);
final_transformation_ = guess;
int i_iter = 0;
if (!guess.isApprox(Eigen::Matrix4f::Identity (), 0.01f))
{ //If guess is not the Identity matrix we check it.
transformPointCloud (*input_, input_transformed, final_transformation_);
lowest_error = computeErrorMetric (input_transformed, static_cast<float> (corr_dist_threshold_));
i_iter = 1;
}
for (; i_iter < max_iterations_; ++i_iter)
{
// Draw nr_samples_ random samples
selectSamples (*input_, nr_samples_, min_sample_distance_, sample_indices);
// Find corresponding features in the target cloud
findSimilarFeatures (*input_features_, sample_indices, corresponding_indices);
// Estimate the transform from the samples to their corresponding points
transformation_estimation_->estimateRigidTransformation (*input_, sample_indices, *target_, corresponding_indices, transformation_);
// Tranform the data and compute the error
transformPointCloud (*input_, input_transformed, transformation_);
error = computeErrorMetric (input_transformed, static_cast<float> (corr_dist_threshold_));
// If the new error is lower, update the final transformation
if (i_iter == 0 || error < lowest_error)
{
lowest_error = error;
final_transformation_ = transformation_;
}
}
// Apply the final transformation
transformPointCloud (*input_, output, final_transformation_);
}