C++ Stamped::setBasis方法代码示例

  /*
   * given the current state of the robot, find a good trajectory
   */
  base_local_planner::Trajectory DWAPlanner::findBestPath(
      tf::Stamped<tf::Pose> global_pose,
      tf::Stamped<tf::Pose> global_vel,
      tf::Stamped<tf::Pose>& drive_velocities,
      std::vector<geometry_msgs::Point> footprint_spec) {

    obstacle_costs_.setFootprint(footprint_spec);

    //make sure that our configuration doesn't change mid-run
    boost::mutex::scoped_lock l(configuration_mutex_);

    Eigen::Vector3f pos(global_pose.getOrigin().getX(), global_pose.getOrigin().getY(), tf::getYaw(global_pose.getRotation()));
    Eigen::Vector3f vel(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));
    geometry_msgs::PoseStamped goal_pose = global_plan_.back();
    Eigen::Vector3f goal(goal_pose.pose.position.x, goal_pose.pose.position.y, tf::getYaw(goal_pose.pose.orientation));
    base_local_planner::LocalPlannerLimits limits = planner_util_->getCurrentLimits();

    // prepare cost functions and generators for this run
    generator_.initialise(pos,
        vel,
        goal,
        &limits,
        vsamples_);

    result_traj_.cost_ = -7;
    // find best trajectory by sampling and scoring the samples
    scored_sampling_planner_.findBestTrajectory(result_traj_, NULL);

    // verbose publishing of point clouds
    if (publish_cost_grid_pc_) {
      //we'll publish the visualization of the costs to rviz before returning our best trajectory
      map_viz_.publishCostCloud(*(planner_util_->getCostmap()));
    }

    // debrief stateful scoring functions
    oscillation_costs_.updateOscillationFlags(pos, &result_traj_, planner_util_->getCurrentLimits().min_trans_vel);

    //if we don't have a legal trajectory, we'll just command zero
    if (result_traj_.cost_ < 0) {
      drive_velocities.setIdentity();
    } else {
      tf::Vector3 start(result_traj_.xv_, result_traj_.yv_, 0);
      drive_velocities.setOrigin(start);
      tf::Matrix3x3 matrix;
      matrix.setRotation(tf::createQuaternionFromYaw(result_traj_.thetav_));
      drive_velocities.setBasis(matrix);
    }

    return result_traj_;
  }

  //given the current state of the robot, find a good trajectory
  base_local_planner::Trajectory DWAPlanner::findBestPath(tf::Stamped<tf::Pose> global_pose, tf::Stamped<tf::Pose> global_vel, 
      tf::Stamped<tf::Pose>& drive_velocities){

    //make sure that our configuration doesn't change mid-run
    boost::mutex::scoped_lock l(configuration_mutex_);

    //make sure to get an updated copy of the costmap before computing trajectories
    costmap_ros_->getCostmapCopy(costmap_);

    Eigen::Vector3f pos(global_pose.getOrigin().getX(), global_pose.getOrigin().getY(), tf::getYaw(global_pose.getRotation()));
    Eigen::Vector3f vel(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));

    //reset the map for new operations
    map_.resetPathDist();
    front_map_.resetPathDist();

    //make sure that we update our path based on the global plan and compute costs
    map_.setPathCells(costmap_, global_plan_);

    std::vector<geometry_msgs::PoseStamped> front_global_plan = global_plan_;
    front_global_plan.back().pose.position.x = front_global_plan.back().pose.position.x + forward_point_distance_ * cos(tf::getYaw(front_global_plan.back().pose.orientation));
    front_global_plan.back().pose.position.y = front_global_plan.back().pose.position.y + forward_point_distance_ * sin(tf::getYaw(front_global_plan.back().pose.orientation));
    front_map_.setPathCells(costmap_, front_global_plan);
    ROS_DEBUG_NAMED("dwa_local_planner", "Path/Goal distance computed");

    //rollout trajectories and find the minimum cost one
    base_local_planner::Trajectory best = computeTrajectories(pos, vel);
    ROS_DEBUG_NAMED("dwa_local_planner", "Trajectories created");

    //if we don't have a legal trajectory, we'll just command zero
    if(best.cost_ < 0){
      drive_velocities.setIdentity();
    }
    else{
      tf::Vector3 start(best.xv_, best.yv_, 0);
      drive_velocities.setOrigin(start);
      tf::Matrix3x3 matrix;
      matrix.setRotation(tf::createQuaternionFromYaw(best.thetav_));
      drive_velocities.setBasis(matrix);
    }

    //we'll publish the visualization of the costs to rviz before returning our best trajectory
    map_viz_.publishCostCloud();

    return best;
  }

  /*
   * given the current state of the robot, find a good trajectory
   */
  base_local_planner::Trajectory EDWAPlanner::findBestPath(
      tf::Stamped<tf::Pose> global_pose,
      tf::Stamped<tf::Pose> global_vel,
      tf::Stamped<tf::Pose>& drive_velocities,
      std::vector<geometry_msgs::Point> footprint_spec) {

    obstacle_costs_.setFootprint(footprint_spec);

    //make sure that our configuration doesn't change mid-run
    boost::mutex::scoped_lock l(configuration_mutex_);

    Eigen::Vector3f pos(global_pose.getOrigin().getX(), global_pose.getOrigin().getY(), tf::getYaw(global_pose.getRotation()));
    Eigen::Vector3f vel(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));
    geometry_msgs::PoseStamped goal_pose = global_plan_.back();
    Eigen::Vector3f goal(goal_pose.pose.position.x, goal_pose.pose.position.y, tf::getYaw(goal_pose.pose.orientation));
    base_local_planner::LocalPlannerLimits limits = planner_util_->getCurrentLimits();

    // prepare cost functions and generators for this run
    generator_.initialise(pos,
        vel,
        goal,
        &limits,
        vsamples_);

    energy_costs_.setLastSpeeds(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));

    result_traj_.cost_ = -7;
    // find best trajectory by sampling and scoring the samples
    std::vector<base_local_planner::Trajectory> all_explored;
    scored_sampling_planner_.findBestTrajectory(result_traj_, &all_explored);

    if(publish_traj_pc_)
    {
        base_local_planner::MapGridCostPoint pt;
        traj_cloud_->points.clear();
        traj_cloud_->width = 0;
        traj_cloud_->height = 0;
        std_msgs::Header header;
        pcl_conversions::fromPCL(traj_cloud_->header, header);
        header.stamp = ros::Time::now();
        traj_cloud_->header = pcl_conversions::toPCL(header);
        for(std::vector<base_local_planner::Trajectory>::iterator t=all_explored.begin(); t != all_explored.end(); ++t)
        {
            if(t->cost_<0)
                continue;
            // Fill out the plan
            for(unsigned int i = 0; i < t->getPointsSize(); ++i) {
                double p_x, p_y, p_th;
                t->getPoint(i, p_x, p_y, p_th);
                pt.x=p_x;
                pt.y=p_y;
                pt.z=0;
                pt.path_cost=p_th;
                pt.total_cost=t->cost_;
                traj_cloud_->push_back(pt);
            }
        }
        traj_cloud_pub_.publish(*traj_cloud_);
    }

    // verbose publishing of point clouds
    if (publish_cost_grid_pc_) {
      //we'll publish the visualization of the costs to rviz before returning our best trajectory
      map_viz_.publishCostCloud(planner_util_->getCostmap());
    }

    // debrief stateful scoring functions
    oscillation_costs_.updateOscillationFlags(pos, &result_traj_, planner_util_->getCurrentLimits().min_trans_vel);

    //if we don't have a legal trajectory, we'll just command zero
    if (result_traj_.cost_ < 0) {
      drive_velocities.setIdentity();
    } else {
      tf::Vector3 start(result_traj_.xv_, result_traj_.yv_, 0);
      drive_velocities.setOrigin(start);
      tf::Matrix3x3 matrix;
      matrix.setRotation(tf::createQuaternionFromYaw(result_traj_.thetav_));
      drive_velocities.setBasis(matrix);
    }

    return result_traj_;
  }

  //given the current state of the robot, find a good trajectory
  Trajectory TrajectoryPlanner::findBestPath(tf::Stamped<tf::Pose> global_pose, tf::Stamped<tf::Pose> global_vel, 
      tf::Stamped<tf::Pose>& drive_velocities){

    Eigen::Vector3f pos(global_pose.getOrigin().getX(), global_pose.getOrigin().getY(), tf::getYaw(global_pose.getRotation()));
    Eigen::Vector3f vel(global_vel.getOrigin().getX(), global_vel.getOrigin().getY(), tf::getYaw(global_vel.getRotation()));

    //reset the map for new operations
    path_map_.resetPathDist();
    goal_map_.resetPathDist();

    //temporarily remove obstacles that are within the footprint of the robot
    std::vector<base_local_planner::Position2DInt> footprint_list =
        footprint_helper_.getFootprintCells(
            pos,
            footprint_spec_,
            costmap_,
            true);

    //mark cells within the initial footprint of the robot
    for (unsigned int i = 0; i < footprint_list.size(); ++i) {
      path_map_(footprint_list[i].x, footprint_list[i].y).within_robot = true;
    }

    //make sure that we update our path based on the global plan and compute costs
    path_map_.setTargetCells(costmap_, global_plan_);
    goal_map_.setLocalGoal(costmap_, global_plan_);
    ROS_DEBUG("Path/Goal distance computed");

    //rollout trajectories and find the minimum cost one
    Trajectory best = createTrajectories(pos[0], pos[1], pos[2],
        vel[0], vel[1], vel[2],
        acc_lim_x_, acc_lim_y_, acc_lim_theta_);
    ROS_DEBUG("Trajectories created");

    /*
    //If we want to print a ppm file to draw goal dist
    char buf[4096];
    sprintf(buf, "base_local_planner.ppm");
    FILE *fp = fopen(buf, "w");
    if(fp){
      fprintf(fp, "P3\n");
      fprintf(fp, "%d %d\n", map_.size_x_, map_.size_y_);
      fprintf(fp, "255\n");
      for(int j = map_.size_y_ - 1; j >= 0; --j){
        for(unsigned int i = 0; i < map_.size_x_; ++i){
          int g_dist = 255 - int(map_(i, j).goal_dist);
          int p_dist = 255 - int(map_(i, j).path_dist);
          if(g_dist < 0)
            g_dist = 0;
          if(p_dist < 0)
            p_dist = 0;
          fprintf(fp, "%d 0 %d ", g_dist, 0);
        }
        fprintf(fp, "\n");
      }
      fclose(fp);
    }
    */

    if(best.cost_ < 0){
      drive_velocities.setIdentity();
    }
    else{
      tf::Vector3 start(best.xv_, best.yv_, 0);
      drive_velocities.setOrigin(start);
      tf::Matrix3x3 matrix;
      matrix.setRotation(tf::createQuaternionFromYaw(best.thetav_));
      drive_velocities.setBasis(matrix);
    }

    return best;
  }