6 #include <pcl/ModelCoefficients.h>
7 #include <pcl/sample_consensus/method_types.h>
8 #include <pcl/sample_consensus/model_types.h>
9 #include <pcl/segmentation/sac_segmentation.h>
10 #include <pcl/filters/extract_indices.h>
11 #include <pcl/segmentation/extract_clusters.h>
27 fitPlane (
const PointCloudPtr & input,
float distance_threshold,
float max_iterations)
40 seg.
segment (*inliers, *coefficients);
42 return (coefficients);
57 findAndSubtractPlane (
const PointCloudPtr & input,
float distance_threshold,
float max_iterations)
69 seg.
segment (*inliers, *coefficients);
93 clusterObjects (
const PointCloudPtr & input,
94 float cluster_tolerance,
int min_cluster_size,
int max_cluster_size,
95 std::vector<pcl::PointIndices> & cluster_indices_out)
103 ec.
extract (cluster_indices_out);
void setOptimizeCoefficients(bool optimize)
Set to true if a coefficient refinement is required.
void setNegative(bool negative)
Set whether the regular conditions for points filtering should apply, or the inverted conditions...
SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models...
void setMethodType(int method)
The type of sample consensus method to use (user given parameter).
static const int SAC_RANSAC
PointCloud represents the base class in PCL for storing collections of 3D points. ...
void setModelType(int model)
The type of model to use (user given parameter).
virtual void setIndices(const IndicesPtr &indices)
Provide a pointer to the vector of indices that represents the input data.
void filter(PointCloud &output)
boost::shared_ptr< ::pcl::PointIndices > Ptr
boost::shared_ptr< ::pcl::ModelCoefficients > Ptr
virtual void segment(PointIndices &inliers, ModelCoefficients &model_coefficients)
Base method for segmentation of a model in a PointCloud given by
void setMaxIterations(int max_iterations)
Set the maximum number of iterations before giving up.
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
void setDistanceThreshold(double threshold)
Distance to the model threshold (user given parameter).