pointcloud_octomap_updater.cpp

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/* Author: Jon Binney, Ioan Sucan */
 
#include <cmath>
#include <moveit/pointcloud_octomap_updater/pointcloud_octomap_updater.hpp>
#include <moveit/occupancy_map_monitor/occupancy_map_monitor.hpp>
#include <tf2_geometry_msgs/tf2_geometry_msgs.hpp>
// TODO: Remove conditional includes when released to all active distros.
#if __has_include(<tf2/LinearMath/Vector3.hpp>)
#include <tf2/LinearMath/Vector3.hpp>
#else
#include <tf2/LinearMath/Vector3.h>
#endif
#if __has_include(<tf2/LinearMath/Transform.hpp>)
#include <tf2/LinearMath/Transform.hpp>
#else
#include <tf2/LinearMath/Transform.h>
#endif
#include <sensor_msgs/point_cloud2_iterator.hpp>
// For Rolling, Kilted, and newer
#if RCLCPP_VERSION_GTE(29, 6, 0)
#include <tf2_ros/create_timer_interface.hpp>
#include <tf2_ros/create_timer_ros.hpp>
// For Jazzy and older
#else
#include <tf2_ros/create_timer_interface.h>
#include <tf2_ros/create_timer_ros.h>
#endif
#include <moveit/utils/logger.hpp>
#include <rclcpp/version.h>
 
#include <memory>
 
namespace occupancy_map_monitor
{
PointCloudOctomapUpdater::PointCloudOctomapUpdater()
  : OccupancyMapUpdater("PointCloudUpdater")
  , scale_(1.0)
  , padding_(0.0)
  , max_range_(std::numeric_limits<double>::infinity())
  , point_subsample_(1)
  , max_update_rate_(0)
  , point_cloud_subscriber_(nullptr)
  , point_cloud_filter_(nullptr)
  , logger_(moveit::getLogger("moveit.ros.pointcloud_octomap_updater"))
{
}
 
bool PointCloudOctomapUpdater::setParams(const std::string& name_space)
{
  auto check_required = [this, &name_space](const std::string& key, auto& target,
                                            std::vector<std::string>& missing_keys) {
    if (!this->node_->get_parameter(name_space + "." + key, target))
    {
      missing_keys.push_back(key);
    }
  };
  // This parameter is optional
  node_->get_parameter_or(name_space + ".ns", ns_, std::string());
 
  std::vector<std::string> missing_keys;
 
  check_required("point_cloud_topic", point_cloud_topic_, missing_keys);
  check_required("max_range", max_range_, missing_keys);
  check_required("padding_offset", padding_, missing_keys);
  check_required("padding_scale", scale_, missing_keys);
  check_required("point_subsample", point_subsample_, missing_keys);
  check_required("max_update_rate", max_update_rate_, missing_keys);
  check_required("filtered_cloud_topic", filtered_cloud_topic_, missing_keys);
 
  if (missing_keys.empty())
  {
    return true;
  }
  std::ostringstream oss;
  for (const auto& name : missing_keys)
  {
    oss << ", "
        << "'" << name << "'";
  }
  RCLCPP_ERROR(node_->get_logger(), "Missing parameters under '%s': %s", name_space.c_str(), oss.str().c_str());
  return false;
}
 
bool PointCloudOctomapUpdater::initialize(const rclcpp::Node::SharedPtr& node)
{
  node_ = node;
  tf_buffer_ = std::make_shared<tf2_ros::Buffer>(node_->get_clock());
  auto create_timer_interface =
      std::make_shared<tf2_ros::CreateTimerROS>(node->get_node_base_interface(), node->get_node_timers_interface());
  tf_buffer_->setCreateTimerInterface(create_timer_interface);
  tf_listener_ = std::make_shared<tf2_ros::TransformListener>(*tf_buffer_);
  shape_mask_ = std::make_unique<point_containment_filter::ShapeMask>();
  shape_mask_->setTransformCallback(
      [this](ShapeHandle shape, Eigen::Isometry3d& tf) { return getShapeTransform(shape, tf); });
 
  return true;
}
 
void PointCloudOctomapUpdater::start()
{
  std::string prefix = "";
  if (!ns_.empty())
    prefix = ns_ + "/";
 
  if (!filtered_cloud_topic_.empty())
  {
    filtered_cloud_publisher_ =
        node_->create_publisher<sensor_msgs::msg::PointCloud2>(prefix + filtered_cloud_topic_, rclcpp::SensorDataQoS());
  }
 
  if (point_cloud_subscriber_)
    return;
 
  rclcpp::SubscriptionOptions options;
  options.callback_group = node_->create_callback_group(rclcpp::CallbackGroupType::MutuallyExclusive);
  /* subscribe to point cloud topic using tf filter*/
  auto qos_profile =
#if RCLCPP_VERSION_GTE(28, 3, 0)
      rclcpp::SensorDataQoS();
#else
      rmw_qos_profile_sensor_data;
#endif
  point_cloud_subscriber_ =
      new message_filters::Subscriber<sensor_msgs::msg::PointCloud2>(node_, point_cloud_topic_, qos_profile, options);
  if (tf_listener_ && tf_buffer_ && !monitor_->getMapFrame().empty())
  {
// For Rolling, L-turtle, and newer
#if RCLCPP_VERSION_GTE(30, 0, 0)
    using MessageFilterPointCloud2 = tf2_ros::MessageFilter<sensor_msgs::msg::PointCloud2>;
 
    MessageFilterPointCloud2::RequiredInterfaces required_interfaces{ node_->get_node_logging_interface(),
                                                                      node_->get_node_clock_interface() };
 
    point_cloud_filter_ = new tf2_ros::MessageFilter<sensor_msgs::msg::PointCloud2>(
        *point_cloud_subscriber_, *tf_buffer_, monitor_->getMapFrame(), 5, std::move(required_interfaces));
#else
    point_cloud_filter_ = new tf2_ros::MessageFilter<sensor_msgs::msg::PointCloud2>(
        *point_cloud_subscriber_, *tf_buffer_, monitor_->getMapFrame(), 5, node_);
#endif
    point_cloud_filter_->registerCallback(
        [this](const sensor_msgs::msg::PointCloud2::ConstSharedPtr& cloud) { cloudMsgCallback(cloud); });
    RCLCPP_INFO(logger_, "Listening to '%s' using message filter with target frame '%s'", point_cloud_topic_.c_str(),
                point_cloud_filter_->getTargetFramesString().c_str());
  }
  else
  {
    point_cloud_subscriber_->registerCallback(
        [this](const sensor_msgs::msg::PointCloud2::ConstSharedPtr& cloud) { cloudMsgCallback(cloud); });
    RCLCPP_INFO(logger_, "Listening to '%s'", point_cloud_topic_.c_str());
  }
}
 
void PointCloudOctomapUpdater::stop()
{
  delete point_cloud_filter_;
  delete point_cloud_subscriber_;
  point_cloud_filter_ = nullptr;
  point_cloud_subscriber_ = nullptr;
}
 
ShapeHandle PointCloudOctomapUpdater::excludeShape(const shapes::ShapeConstPtr& shape)
{
  ShapeHandle h = 0;
  if (shape_mask_)
  {
    h = shape_mask_->addShape(shape, scale_, padding_);
  }
  else
  {
    RCLCPP_ERROR(logger_, "Shape filter not yet initialized!");
  }
  return h;
}
 
void PointCloudOctomapUpdater::forgetShape(ShapeHandle handle)
{
  if (shape_mask_)
    shape_mask_->removeShape(handle);
}
 
bool PointCloudOctomapUpdater::getShapeTransform(ShapeHandle h, Eigen::Isometry3d& transform) const
{
  ShapeTransformCache::const_iterator it = transform_cache_.find(h);
  if (it != transform_cache_.end())
  {
    transform = it->second;
  }
  return it != transform_cache_.end();
}
 
void PointCloudOctomapUpdater::updateMask(const sensor_msgs::msg::PointCloud2& /*cloud*/,
                                          const Eigen::Vector3d& /*sensor_origin*/, std::vector<int>& /*mask*/)
{
}
 
void PointCloudOctomapUpdater::cloudMsgCallback(const sensor_msgs::msg::PointCloud2::ConstSharedPtr& cloud_msg)
{
  RCLCPP_DEBUG(logger_, "Received a new point cloud message");
  rclcpp::Time start = rclcpp::Clock(RCL_ROS_TIME).now();
 
  if (max_update_rate_ > 0)
  {
    // ensure we are not updating the octomap representation too often
    if ((node_->now() - last_update_time_) <= rclcpp::Duration::from_seconds(1.0 / max_update_rate_))
      return;
    last_update_time_ = node_->now();
  }
 
  if (monitor_->getMapFrame().empty())
    monitor_->setMapFrame(cloud_msg->header.frame_id);
 
  /* get transform for cloud into map frame */
  tf2::Stamped<tf2::Transform> map_h_sensor;
  if (monitor_->getMapFrame() == cloud_msg->header.frame_id)
  {
    map_h_sensor.setIdentity();
  }
  else
  {
    if (tf_buffer_)
    {
      try
      {
        tf2::fromMsg(tf_buffer_->lookupTransform(monitor_->getMapFrame(), cloud_msg->header.frame_id,
                                                 cloud_msg->header.stamp),
                     map_h_sensor);
      }
      catch (tf2::TransformException& ex)
      {
        RCLCPP_ERROR_STREAM(logger_, "Transform error of sensor data: " << ex.what() << "; quitting callback");
        return;
      }
    }
    else
      return;
  }
 
  /* compute sensor origin in map frame */
  const tf2::Vector3& sensor_origin_tf = map_h_sensor.getOrigin();
  octomap::point3d sensor_origin(sensor_origin_tf.getX(), sensor_origin_tf.getY(), sensor_origin_tf.getZ());
  Eigen::Vector3d sensor_origin_eigen(sensor_origin_tf.getX(), sensor_origin_tf.getY(), sensor_origin_tf.getZ());
 
  if (!updateTransformCache(cloud_msg->header.frame_id, cloud_msg->header.stamp))
    return;
 
  /* mask out points on the robot */
  shape_mask_->maskContainment(*cloud_msg, sensor_origin_eigen, 0.0, max_range_, mask_);
  updateMask(*cloud_msg, sensor_origin_eigen, mask_);
 
  octomap::KeySet free_cells, occupied_cells, model_cells, clip_cells;
  std::unique_ptr<sensor_msgs::msg::PointCloud2> filtered_cloud;
 
  // We only use these iterators if we are creating a filtered_cloud for
  // publishing. We cannot default construct these, so we use unique_ptr's
  // to defer construction
  std::unique_ptr<sensor_msgs::PointCloud2Iterator<float>> iter_filtered_x;
  std::unique_ptr<sensor_msgs::PointCloud2Iterator<float>> iter_filtered_y;
  std::unique_ptr<sensor_msgs::PointCloud2Iterator<float>> iter_filtered_z;
 
  if (!filtered_cloud_topic_.empty())
  {
    filtered_cloud = std::make_unique<sensor_msgs::msg::PointCloud2>();
    filtered_cloud->header = cloud_msg->header;
    sensor_msgs::PointCloud2Modifier pcd_modifier(*filtered_cloud);
    pcd_modifier.setPointCloud2FieldsByString(1, "xyz");
    pcd_modifier.resize(cloud_msg->width * cloud_msg->height);
 
    // we have created a filtered_out, so we can create the iterators now
    iter_filtered_x = std::make_unique<sensor_msgs::PointCloud2Iterator<float>>(*filtered_cloud, "x");
    iter_filtered_y = std::make_unique<sensor_msgs::PointCloud2Iterator<float>>(*filtered_cloud, "y");
    iter_filtered_z = std::make_unique<sensor_msgs::PointCloud2Iterator<float>>(*filtered_cloud, "z");
  }
  size_t filtered_cloud_size = 0;
 
  tree_->lockRead();
 
  try
  {
    /* do ray tracing to find which cells this point cloud indicates should be free, and which it indicates
     * should be occupied */
    for (unsigned int row = 0; row < cloud_msg->height; row += point_subsample_)
    {
      unsigned int row_c = row * cloud_msg->width;
      sensor_msgs::PointCloud2ConstIterator<float> pt_iter(*cloud_msg, "x");
      // set iterator to point at start of the current row
      pt_iter += row_c;
 
      for (unsigned int col = 0; col < cloud_msg->width; col += point_subsample_, pt_iter += point_subsample_)
      {
        // if (mask_[row_c + col] == point_containment_filter::ShapeMask::CLIP)
        //  continue;
 
        /* check for NaN */
        if (!std::isnan(pt_iter[0]) && !std::isnan(pt_iter[1]) && !std::isnan(pt_iter[2]))
        {
          /* occupied cell at ray endpoint if ray is shorter than max range and this point
             isn't on a part of the robot*/
          if (mask_[row_c + col] == point_containment_filter::ShapeMask::INSIDE)
          {
            // transform to map frame
            tf2::Vector3 point_tf = map_h_sensor * tf2::Vector3(pt_iter[0], pt_iter[1], pt_iter[2]);
            model_cells.insert(tree_->coordToKey(point_tf.getX(), point_tf.getY(), point_tf.getZ()));
          }
          else if (mask_[row_c + col] == point_containment_filter::ShapeMask::CLIP)
          {
            tf2::Vector3 clipped_point_tf =
                map_h_sensor * (tf2::Vector3(pt_iter[0], pt_iter[1], pt_iter[2]).normalize() * max_range_);
            clip_cells.insert(
                tree_->coordToKey(clipped_point_tf.getX(), clipped_point_tf.getY(), clipped_point_tf.getZ()));
          }
          else
          {
            tf2::Vector3 point_tf = map_h_sensor * tf2::Vector3(pt_iter[0], pt_iter[1], pt_iter[2]);
            occupied_cells.insert(tree_->coordToKey(point_tf.getX(), point_tf.getY(), point_tf.getZ()));
            // build list of valid points if we want to publish them
            if (filtered_cloud)
            {
              **iter_filtered_x = pt_iter[0];
              **iter_filtered_y = pt_iter[1];
              **iter_filtered_z = pt_iter[2];
              ++filtered_cloud_size;
              ++*iter_filtered_x;
              ++*iter_filtered_y;
              ++*iter_filtered_z;
            }
          }
        }
      }
    }
 
    /* compute the free cells along each ray that ends at an occupied cell */
    for (const octomap::OcTreeKey& occupied_cell : occupied_cells)
    {
      if (tree_->computeRayKeys(sensor_origin, tree_->keyToCoord(occupied_cell), key_ray_))
        free_cells.insert(key_ray_.begin(), key_ray_.end());
    }
 
    /* compute the free cells along each ray that ends at a model cell */
    for (const octomap::OcTreeKey& model_cell : model_cells)
    {
      if (tree_->computeRayKeys(sensor_origin, tree_->keyToCoord(model_cell), key_ray_))
        free_cells.insert(key_ray_.begin(), key_ray_.end());
    }
 
    /* compute the free cells along each ray that ends at a clipped cell */
    for (const octomap::OcTreeKey& clip_cell : clip_cells)
    {
      free_cells.insert(clip_cell);
      if (tree_->computeRayKeys(sensor_origin, tree_->keyToCoord(clip_cell), key_ray_))
        free_cells.insert(key_ray_.begin(), key_ray_.end());
    }
  }
  catch (...)
  {
    tree_->unlockRead();
    return;
  }
 
  tree_->unlockRead();
 
  /* cells that overlap with the model are not occupied */
  for (const octomap::OcTreeKey& model_cell : model_cells)
    occupied_cells.erase(model_cell);
 
  /* occupied cells are not free */
  for (const octomap::OcTreeKey& occupied_cell : occupied_cells)
    free_cells.erase(occupied_cell);
 
  tree_->lockWrite();
 
  try
  {
    /* mark free cells only if not seen occupied in this cloud */
    for (const octomap::OcTreeKey& free_cell : free_cells)
      tree_->updateNode(free_cell, false);
 
    /* now mark all occupied cells */
    for (const octomap::OcTreeKey& occupied_cell : occupied_cells)
      tree_->updateNode(occupied_cell, true);
 
    // set the logodds to the minimum for the cells that are part of the model
    const float lg = tree_->getClampingThresMinLog() - tree_->getClampingThresMaxLog();
    for (const octomap::OcTreeKey& model_cell : model_cells)
      tree_->updateNode(model_cell, lg);
  }
  catch (...)
  {
    RCLCPP_ERROR(logger_, "Internal error while updating octree");
  }
  tree_->unlockWrite();
  RCLCPP_DEBUG(logger_, "Processed point cloud in %lf ms", (node_->now() - start).seconds() * 1000.0);
  tree_->triggerUpdateCallback();
 
  if (filtered_cloud)
  {
    sensor_msgs::PointCloud2Modifier pcd_modifier(*filtered_cloud);
    pcd_modifier.resize(filtered_cloud_size);
    filtered_cloud_publisher_->publish(*filtered_cloud);
  }
}
}  // namespace occupancy_map_monitor