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342
navigations/costmap_converter/src/costmap_to_lines_ransac.cpp
Executable file
342
navigations/costmap_converter/src/costmap_to_lines_ransac.cpp
Executable file
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/*********************************************************************
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*
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* Software License Agreement (BSD License)
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*
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* Copyright (c) 2016,
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* TU Dortmund - Institute of Control Theory and Systems Engineering.
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the institute nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* Author: Christoph Rösmann
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*********************************************************************/
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#include <costmap_converter/costmap_to_lines_ransac.h>
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#include <boost/thread.hpp>
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#include <boost/thread/mutex.hpp>
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#include <pluginlib/class_list_macros.h>
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PLUGINLIB_EXPORT_CLASS(costmap_converter::CostmapToLinesDBSRANSAC, costmap_converter::BaseCostmapToPolygons)
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namespace costmap_converter
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{
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CostmapToLinesDBSRANSAC::CostmapToLinesDBSRANSAC() : CostmapToPolygonsDBSMCCH()
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{
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dynamic_recfg_ = NULL;
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}
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CostmapToLinesDBSRANSAC::~CostmapToLinesDBSRANSAC()
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{
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if (dynamic_recfg_ != NULL)
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delete dynamic_recfg_;
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}
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void CostmapToLinesDBSRANSAC::initialize(ros::NodeHandle nh)
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{
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// DB SCAN
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nh.param("cluster_max_distance", parameter_.max_distance_, 0.4);
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nh.param("cluster_min_pts", parameter_.min_pts_, 2);
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nh.param("cluster_max_pts", parameter_.max_pts_, 30);
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// convex hull (only necessary if outlier filtering is enabled)
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nh.param("convex_hull_min_pt_separation", parameter_.min_keypoint_separation_, 0.1);
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parameter_buffered_ = parameter_;
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// ransac
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nh.param("ransac_inlier_distance", ransac_inlier_distance_, 0.2);
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nh.param("ransac_min_inliers", ransac_min_inliers_, 10);
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nh.param("ransac_no_iterations", ransac_no_iterations_, 2000);
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nh.param("ransac_remainig_outliers", ransac_remainig_outliers_, 3);
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nh.param("ransac_convert_outlier_pts", ransac_convert_outlier_pts_, true);
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nh.param("ransac_filter_remaining_outlier_pts", ransac_filter_remaining_outlier_pts_, false);
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// setup dynamic reconfigure
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dynamic_recfg_ = new dynamic_reconfigure::Server<CostmapToLinesDBSRANSACConfig>(nh);
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dynamic_reconfigure::Server<CostmapToLinesDBSRANSACConfig>::CallbackType cb = boost::bind(&CostmapToLinesDBSRANSAC::reconfigureCB, this, _1, _2);
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dynamic_recfg_->setCallback(cb);
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}
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void CostmapToLinesDBSRANSAC::compute()
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{
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std::vector< std::vector<KeyPoint> > clusters;
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dbScan(clusters);
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// Create new polygon container
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PolygonContainerPtr polygons(new std::vector<geometry_msgs::Polygon>());
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// fit lines using ransac for each cluster
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for (int i = 1; i <clusters.size(); ++i) // skip first cluster, since it is just noise
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{
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while (clusters[i].size() > ransac_remainig_outliers_)
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{
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// std::vector<KeyPoint> inliers;
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std::vector<KeyPoint> outliers;
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std::pair<KeyPoint,KeyPoint> model;
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if (!lineRansac(clusters[i], ransac_inlier_distance_, ransac_no_iterations_, ransac_min_inliers_, model, NULL, &outliers ) )
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break;
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// add to polygon container
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geometry_msgs::Polygon line;
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line.points.resize(2);
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model.first.toPointMsg(line.points.front());
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model.second.toPointMsg(line.points.back());
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polygons->push_back(line);
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clusters[i] = outliers;
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}
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// create point polygons for remaining outliers
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if (ransac_convert_outlier_pts_)
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{
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if (ransac_filter_remaining_outlier_pts_)
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{
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// take edge points of a convex polygon
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// these points define a cluster and since all lines are extracted,
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// we remove points from the interior...
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geometry_msgs::Polygon polygon;
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convexHull2(clusters[i], polygon);
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for (int j=0; j < (int)polygon.points.size(); ++j)
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{
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polygons->push_back(geometry_msgs::Polygon());
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convertPointToPolygon(polygon.points[j], polygons->back());
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}
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}
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else
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{
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for (int j = 0; j < (int)clusters[i].size(); ++j)
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{
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polygons->push_back(geometry_msgs::Polygon());
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convertPointToPolygon(clusters[i][j], polygons->back());
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}
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}
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}
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}
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// add our non-cluster points to the polygon container (as single points)
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if (!clusters.empty())
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{
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for (int i=0; i < clusters.front().size(); ++i)
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{
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polygons->push_back( geometry_msgs::Polygon() );
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convertPointToPolygon(clusters.front()[i], polygons->back());
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}
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}
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// replace shared polygon container
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updatePolygonContainer(polygons);
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}
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bool CostmapToLinesDBSRANSAC::lineRansac(const std::vector<KeyPoint>& data, double inlier_distance, int no_iterations,
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int min_inliers, std::pair<KeyPoint, KeyPoint>& best_model,
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std::vector<KeyPoint>* inliers, std::vector<KeyPoint>* outliers)
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{
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if (data.size() < 2 || data.size() < min_inliers)
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{
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return false;
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}
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boost::random::uniform_int_distribution<> distribution(0, data.size()-1);
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std::pair<int, int> best_model_idx;
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int best_no_inliers = -1;
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for (int i=0; i < no_iterations; ++i)
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{
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// choose random points to define a line candidate
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int start_idx = distribution(rnd_generator_);
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int end_idx = start_idx;
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while (end_idx == start_idx)
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end_idx = distribution(rnd_generator_);
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// compute inliers
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int no_inliers = 0;
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for (int j=0; j<(int)data.size(); ++j)
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{
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if ( isInlier(data[j], data[start_idx], data[end_idx], inlier_distance) )
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++no_inliers;
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}
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if (no_inliers > best_no_inliers)
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{
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best_no_inliers = no_inliers;
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best_model_idx.first = start_idx;
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best_model_idx.second = end_idx;
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}
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}
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best_model.first = data[best_model_idx.first];
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best_model.second = data[best_model_idx.second];
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if (best_no_inliers < min_inliers)
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return false;
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// Now repeat the calculation for the best model in order to obtain the inliers and outliers set
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// This might be faster if no_iterations is large, but TEST
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if (inliers || outliers)
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{
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if (inliers)
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inliers->clear();
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if (outliers)
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outliers->clear();
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int no_inliers = 0;
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for (int i=0; i<(int)data.size(); ++i)
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{
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if ( isInlier( data[i], best_model.first, best_model.second, inlier_distance ) )
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{
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if (inliers)
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inliers->push_back( data[i] );
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}
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else
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{
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if (outliers)
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outliers->push_back( data[i] );
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}
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}
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}
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return true;
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}
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bool CostmapToLinesDBSRANSAC::linearRegression(const std::vector<KeyPoint>& data, double& slope, double& intercept,
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double* mean_x_out, double* mean_y_out)
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{
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if (data.size() < 2)
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{
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ROS_ERROR("CostmapToLinesDBSRANSAC: at least 2 data points required for linear regression");
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return false;
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}
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double mean_x = 0;
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double mean_y = 0;
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for (int i=0; i<(int)data.size(); ++i)
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{
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mean_x += data[i].x;
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mean_y += data[i].y;
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}
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mean_x /= double(data.size());
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mean_y /= double(data.size());
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if (mean_x_out)
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*mean_x_out = mean_x;
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if (mean_y_out)
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*mean_y_out = mean_y;
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double numerator = 0.0;
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double denominator = 0.0;
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for(int i=0; i<(int)data.size(); ++i)
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{
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double dx = data[i].x - mean_x;
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numerator += dx * (data[i].y - mean_y);
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denominator += dx*dx;
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}
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if (denominator == 0)
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{
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ROS_ERROR("CostmapToLinesDBSRANSAC: linear regression failed, denominator 0");
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return false;
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}
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else
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slope = numerator / denominator;
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intercept = mean_y - slope * mean_x;
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return true;
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}
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void CostmapToLinesDBSRANSAC::reconfigureCB(CostmapToLinesDBSRANSACConfig& config, uint32_t level)
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{
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boost::mutex::scoped_lock lock(parameter_mutex_);
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parameter_buffered_.max_distance_ = config.cluster_max_distance;
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parameter_buffered_.min_pts_ = config.cluster_min_pts;
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parameter_buffered_.max_pts_ = config.cluster_max_pts;
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parameter_buffered_.min_keypoint_separation_ = config.cluster_min_pts;
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ransac_inlier_distance_ = config.ransac_inlier_distance;
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ransac_min_inliers_ = config.ransac_min_inliers;
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ransac_no_iterations_ = config.ransac_no_iterations;
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ransac_remainig_outliers_ = config.ransac_remainig_outliers;
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ransac_convert_outlier_pts_ = config.ransac_convert_outlier_pts;
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ransac_filter_remaining_outlier_pts_ = config.ransac_filter_remaining_outlier_pts;
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}
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/*
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void CostmapToLinesDBSRANSAC::adjustLineLength(const std::vector<KeyPoint>& data, const KeyPoint& linept1, const KeyPoint& linept2,
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KeyPoint& line_start, KeyPoint& line_end)
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{
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line_start = linept1;
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line_end = linept2;
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// infinite line is defined by linept1 and linept2
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double dir_x = line_end.x - line_start.x;
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double dir_y = line_end.y - line_start.y;
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double norm = std::sqrt(dir_x*dir_x + dir_y*dir_y);
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dir_x /= norm;
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dir_y /= norm;
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// project data onto the line and check if the distance is increased in both directions
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for (int i=0; i < (int) data.size(); ++i)
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{
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double dx = data[i].x - line_start.x;
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double dy = data[i].y - line_start.y;
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// check scalar product at start
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double extension = dx*dir_x + dy*dir_y;
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if (extension<0)
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{
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line_start.x -= dir_x*extension;
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line_start.y -= dir_y*extension;
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}
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else
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{
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dx = data[i].x - line_end.x;
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dy = data[i].y - line_end.y;
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// check scalar product at end
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double extension = dx*dir_x + dy*dir_y;
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if (extension>0)
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{
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line_end.x += dir_x*extension;
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line_end.y += dir_y*extension;
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}
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}
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}
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}*/
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}//end namespace costmap_converter
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