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/*********************************************************************
*
* Software License Agreement (BSD License)
*
* Copyright (c) 2016,
* TU Dortmund - Institute of Control Theory and Systems Engineering.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the institute nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* Author: Christoph Rösmann
*********************************************************************/
#include <costmap_converter/costmap_to_lines_ransac.h>
#include <boost/thread.hpp>
#include <boost/thread/mutex.hpp>
#include <pluginlib/class_list_macros.h>
PLUGINLIB_EXPORT_CLASS(costmap_converter::CostmapToLinesDBSRANSAC, costmap_converter::BaseCostmapToPolygons)
namespace costmap_converter
{
CostmapToLinesDBSRANSAC::CostmapToLinesDBSRANSAC() : CostmapToPolygonsDBSMCCH()
{
dynamic_recfg_ = NULL;
}
CostmapToLinesDBSRANSAC::~CostmapToLinesDBSRANSAC()
{
if (dynamic_recfg_ != NULL)
delete dynamic_recfg_;
}
void CostmapToLinesDBSRANSAC::initialize(ros::NodeHandle nh)
{
// DB SCAN
nh.param("cluster_max_distance", parameter_.max_distance_, 0.4);
nh.param("cluster_min_pts", parameter_.min_pts_, 2);
nh.param("cluster_max_pts", parameter_.max_pts_, 30);
// convex hull (only necessary if outlier filtering is enabled)
nh.param("convex_hull_min_pt_separation", parameter_.min_keypoint_separation_, 0.1);
parameter_buffered_ = parameter_;
// ransac
nh.param("ransac_inlier_distance", ransac_inlier_distance_, 0.2);
nh.param("ransac_min_inliers", ransac_min_inliers_, 10);
nh.param("ransac_no_iterations", ransac_no_iterations_, 2000);
nh.param("ransac_remainig_outliers", ransac_remainig_outliers_, 3);
nh.param("ransac_convert_outlier_pts", ransac_convert_outlier_pts_, true);
nh.param("ransac_filter_remaining_outlier_pts", ransac_filter_remaining_outlier_pts_, false);
// setup dynamic reconfigure
dynamic_recfg_ = new dynamic_reconfigure::Server<CostmapToLinesDBSRANSACConfig>(nh);
dynamic_reconfigure::Server<CostmapToLinesDBSRANSACConfig>::CallbackType cb = boost::bind(&CostmapToLinesDBSRANSAC::reconfigureCB, this, _1, _2);
dynamic_recfg_->setCallback(cb);
}
void CostmapToLinesDBSRANSAC::compute()
{
std::vector< std::vector<KeyPoint> > clusters;
dbScan(clusters);
// Create new polygon container
PolygonContainerPtr polygons(new std::vector<geometry_msgs::Polygon>());
// fit lines using ransac for each cluster
for (int i = 1; i <clusters.size(); ++i) // skip first cluster, since it is just noise
{
while (clusters[i].size() > ransac_remainig_outliers_)
{
// std::vector<KeyPoint> inliers;
std::vector<KeyPoint> outliers;
std::pair<KeyPoint,KeyPoint> model;
if (!lineRansac(clusters[i], ransac_inlier_distance_, ransac_no_iterations_, ransac_min_inliers_, model, NULL, &outliers ) )
break;
// add to polygon container
geometry_msgs::Polygon line;
line.points.resize(2);
model.first.toPointMsg(line.points.front());
model.second.toPointMsg(line.points.back());
polygons->push_back(line);
clusters[i] = outliers;
}
// create point polygons for remaining outliers
if (ransac_convert_outlier_pts_)
{
if (ransac_filter_remaining_outlier_pts_)
{
// take edge points of a convex polygon
// these points define a cluster and since all lines are extracted,
// we remove points from the interior...
geometry_msgs::Polygon polygon;
convexHull2(clusters[i], polygon);
for (int j=0; j < (int)polygon.points.size(); ++j)
{
polygons->push_back(geometry_msgs::Polygon());
convertPointToPolygon(polygon.points[j], polygons->back());
}
}
else
{
for (int j = 0; j < (int)clusters[i].size(); ++j)
{
polygons->push_back(geometry_msgs::Polygon());
convertPointToPolygon(clusters[i][j], polygons->back());
}
}
}
}
// add our non-cluster points to the polygon container (as single points)
if (!clusters.empty())
{
for (int i=0; i < clusters.front().size(); ++i)
{
polygons->push_back( geometry_msgs::Polygon() );
convertPointToPolygon(clusters.front()[i], polygons->back());
}
}
// replace shared polygon container
updatePolygonContainer(polygons);
}
bool CostmapToLinesDBSRANSAC::lineRansac(const std::vector<KeyPoint>& data, double inlier_distance, int no_iterations,
int min_inliers, std::pair<KeyPoint, KeyPoint>& best_model,
std::vector<KeyPoint>* inliers, std::vector<KeyPoint>* outliers)
{
if (data.size() < 2 || data.size() < min_inliers)
{
return false;
}
boost::random::uniform_int_distribution<> distribution(0, data.size()-1);
std::pair<int, int> best_model_idx;
int best_no_inliers = -1;
for (int i=0; i < no_iterations; ++i)
{
// choose random points to define a line candidate
int start_idx = distribution(rnd_generator_);
int end_idx = start_idx;
while (end_idx == start_idx)
end_idx = distribution(rnd_generator_);
// compute inliers
int no_inliers = 0;
for (int j=0; j<(int)data.size(); ++j)
{
if ( isInlier(data[j], data[start_idx], data[end_idx], inlier_distance) )
++no_inliers;
}
if (no_inliers > best_no_inliers)
{
best_no_inliers = no_inliers;
best_model_idx.first = start_idx;
best_model_idx.second = end_idx;
}
}
best_model.first = data[best_model_idx.first];
best_model.second = data[best_model_idx.second];
if (best_no_inliers < min_inliers)
return false;
// Now repeat the calculation for the best model in order to obtain the inliers and outliers set
// This might be faster if no_iterations is large, but TEST
if (inliers || outliers)
{
if (inliers)
inliers->clear();
if (outliers)
outliers->clear();
int no_inliers = 0;
for (int i=0; i<(int)data.size(); ++i)
{
if ( isInlier( data[i], best_model.first, best_model.second, inlier_distance ) )
{
if (inliers)
inliers->push_back( data[i] );
}
else
{
if (outliers)
outliers->push_back( data[i] );
}
}
}
return true;
}
bool CostmapToLinesDBSRANSAC::linearRegression(const std::vector<KeyPoint>& data, double& slope, double& intercept,
double* mean_x_out, double* mean_y_out)
{
if (data.size() < 2)
{
ROS_ERROR("CostmapToLinesDBSRANSAC: at least 2 data points required for linear regression");
return false;
}
double mean_x = 0;
double mean_y = 0;
for (int i=0; i<(int)data.size(); ++i)
{
mean_x += data[i].x;
mean_y += data[i].y;
}
mean_x /= double(data.size());
mean_y /= double(data.size());
if (mean_x_out)
*mean_x_out = mean_x;
if (mean_y_out)
*mean_y_out = mean_y;
double numerator = 0.0;
double denominator = 0.0;
for(int i=0; i<(int)data.size(); ++i)
{
double dx = data[i].x - mean_x;
numerator += dx * (data[i].y - mean_y);
denominator += dx*dx;
}
if (denominator == 0)
{
ROS_ERROR("CostmapToLinesDBSRANSAC: linear regression failed, denominator 0");
return false;
}
else
slope = numerator / denominator;
intercept = mean_y - slope * mean_x;
return true;
}
void CostmapToLinesDBSRANSAC::reconfigureCB(CostmapToLinesDBSRANSACConfig& config, uint32_t level)
{
boost::mutex::scoped_lock lock(parameter_mutex_);
parameter_buffered_.max_distance_ = config.cluster_max_distance;
parameter_buffered_.min_pts_ = config.cluster_min_pts;
parameter_buffered_.max_pts_ = config.cluster_max_pts;
parameter_buffered_.min_keypoint_separation_ = config.cluster_min_pts;
ransac_inlier_distance_ = config.ransac_inlier_distance;
ransac_min_inliers_ = config.ransac_min_inliers;
ransac_no_iterations_ = config.ransac_no_iterations;
ransac_remainig_outliers_ = config.ransac_remainig_outliers;
ransac_convert_outlier_pts_ = config.ransac_convert_outlier_pts;
ransac_filter_remaining_outlier_pts_ = config.ransac_filter_remaining_outlier_pts;
}
/*
void CostmapToLinesDBSRANSAC::adjustLineLength(const std::vector<KeyPoint>& data, const KeyPoint& linept1, const KeyPoint& linept2,
KeyPoint& line_start, KeyPoint& line_end)
{
line_start = linept1;
line_end = linept2;
// infinite line is defined by linept1 and linept2
double dir_x = line_end.x - line_start.x;
double dir_y = line_end.y - line_start.y;
double norm = std::sqrt(dir_x*dir_x + dir_y*dir_y);
dir_x /= norm;
dir_y /= norm;
// project data onto the line and check if the distance is increased in both directions
for (int i=0; i < (int) data.size(); ++i)
{
double dx = data[i].x - line_start.x;
double dy = data[i].y - line_start.y;
// check scalar product at start
double extension = dx*dir_x + dy*dir_y;
if (extension<0)
{
line_start.x -= dir_x*extension;
line_start.y -= dir_y*extension;
}
else
{
dx = data[i].x - line_end.x;
dy = data[i].y - line_end.y;
// check scalar product at end
double extension = dx*dir_x + dy*dir_y;
if (extension>0)
{
line_end.x += dir_x*extension;
line_end.y += dir_y*extension;
}
}
}
}*/
}//end namespace costmap_converter