How to use SVM or AdaBoost to train my own data?

README.md

@@ -15,12 +15,13 @@ Video:<br>
 The following ros packages are required:
 - pcl_ros
 - <a href="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/koide3/ndt_omp">ndt_omp</a>
-- <a href="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/koide3/hdl_localization">hdl_localization</a>
+- <a href="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/agn-7/hdl_localization">hdl_localization</a>
 
 ## Example
 
 Bag file (recorded in an outdoor environment): 
-- [hdl_400.bag.tar.gz](https://www.aisl.cs.tut.ac.jp/databases/hdl_graph_slam/hdl_400.bag.tar.gz) (933MB)
+- [hdl_400.bag.tar.gz](https://www.aisl.cs.tut.ac.jp/databases/hdl_graph_slam/hdl_400.bag.tar.gz) (933MB - Moving Sensor)
+- [hdl_500.bag.tar.gz](https://drive.google.com/open?id=1oPj6xJ0VpvGc_u31EQuGAqiVMnbA5Kar) (500MB - Fixed sensor)
 
 ```bash
 rosparam set use_sim_time true
@@ -48,6 +49,15 @@ rosparam set use_sim_time true
 roslaunch hdl_people_tracking hdl_people_tracking_static.launch
 ```
 
+```bash
+roscd hdl_localization/rviz
+rviz -d hdl_localization_static.rviz
+```
+
+```bash
+rosbag play --clock hdl_500.bag
+```
+
 
 ## Related packages
 

apps/hdl_people_detection_nodelet.cpp

@@ -95,8 +95,10 @@ class HdlPeopleDetectionNodelet : public nodelet::Nodelet {
  return;
  }
 
+ //iter++; // frame counter.
+ //std::cout<<iter<<"\n";
  pcl::PointCloud<PointT>::Ptr cloud(new pcl::PointCloud<PointT>());
- pcl::fromROSMsg(*points_msg, *cloud);
+ pcl::fromROSMsg(*points_msg, *cloud); // Convert pcl2 to pcl
  if(cloud->empty()) {
  NODELET_ERROR("cloud is empty!!");
  return;
@@ -107,11 +109,11 @@ class HdlPeopleDetectionNodelet : public nodelet::Nodelet {
  downsample_filter->setInputCloud(cloud);
  downsample_filter->filter(*downsampled);
  downsampled->header = cloud->header;
- cloud = downsampled;
+ //cloud = downsampled; // TODO :: aGn
 
  // background subtraction and people detection
- auto filtered = backsub->filter(cloud);
- auto clusters = detector->detect(filtered);
+ auto filtered = backsub->filter(cloud); // preprocess
+ auto clusters = detector->detect(filtered); // cluster and classify
 
  publish_msgs(points_msg->header.stamp, filtered, clusters);
  }
@@ -139,7 +141,7 @@ class HdlPeopleDetectionNodelet : public nodelet::Nodelet {
  downsample_filter->setInputCloud(cloud);
  downsample_filter->filter(*downsampled);
  downsampled->header = cloud->header;
- cloud = downsampled;
+ cloud = downsampled; // TODO :: aGn
 
  // transform #cloud into the globalmap space
  const auto& position = odom_msg->pose.pose.position;
@@ -183,7 +185,10 @@ class HdlPeopleDetectionNodelet : public nodelet::Nodelet {
  * @param filtered
  * @param clusters
  */
- void publish_msgs(const ros::Time& stamp, const pcl::PointCloud<pcl::PointXYZI>::Ptr& filtered, const std::vector<Cluster::Ptr>& clusters) const {
+ void publish_msgs(
+ const ros::Time& stamp,
+ const pcl::PointCloud<pcl::PointXYZI>::Ptr& filtered,
+ const std::vector<Cluster::Ptr>& clusters) const {
  if(clusters_pub.getNumSubscribers()) {
  hdl_people_tracking::ClusterArrayPtr clusters_msg(new hdl_people_tracking::ClusterArray());
  clusters_msg->header.frame_id = globalmap->header.frame_id;
@@ -292,6 +297,7 @@ class HdlPeopleDetectionNodelet : public nodelet::Nodelet {
  }
 
 private:
+ int iter = 0;
  // ROS
  ros::NodeHandle nh;
  ros::NodeHandle mt_nh;

launch/hdl_people_tracking_static.launch

@@ -9,7 +9,7 @@
  <param name="downsample_resolution" value="0.1"/>
  <!-- back subtraction params -->
  <param name="backsub_resolution" value="0.2"/>
- <param name="backsub_occupancy_thresh" value="2"/>
+ <param name="backsub_occupancy_thresh" value="5"/>
  <!-- clustering params -->
  <param name="cluster_min_pts" value="10"/>
  <param name="cluster_max_pts" value="2048"/>
@@ -20,7 +20,7 @@
  <param name="cluster_max_size_y" value="1.0"/>
  <param name="cluster_max_size_z" value="2.0"/>
  <!-- classification params -->
- <param name="enable_classification" value="false"/>
+ <param name="enable_classification" value="true"/>
  </node>
 
  <node pkg="nodelet" type="nodelet" name="hdl_people_tracking_nodelet" args="load hdl_people_tracking/HdlPeopleTrackingNodelet $(arg nodelet_manager)">

src/people_detector.cpp

@@ -32,6 +32,7 @@ std::vector<Cluster::Ptr> PeopleDetector::detect(const pcl::PointCloud<pcl::Poin
  auto clusters = marcel.detect(cloud);
 
  for(auto& cluster : clusters) {
+ //std::cout<<!classifier<<"\n";
  cluster->is_human = !classifier || classifier->predict(cluster->cloud);
  }