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VP-SOM

Authors: [Zhang Jiadong], [Wang Wei]

VP-SOM is a novel View Planning Method for Indoor Sparse Object Model based on Information Abundance and Observation Continuity.

1. Prerequisites

We have tested the system in Ubuntu 18.04. + ROS melodic for Motion module and the connection between Motion module&View-Planning module. We suggest installing the "desktop-full" version of ROS. + Prerequisites of Active SLAM are the same as ORB_SLAM2, including C++11, OpenCV, Eigen3.4.10, Eigen3, Pangolin, DBoW2, g2o and PCL1.8. + Gazebo 9.0 for Simulization environment. + Rviz for visulization of object map, camera view and robot motion.

2. Building

  • Create ROS workspace and download our package:

    cd {ROS_WORKSPACE}/src
git clone https://github.com/TINY-KE/VP-SOM.git
cd VP-SOM

  • Complie the thirdparty libraries of Active SLAM:

    cd Active_SLAM_based_on_VP-SOM
chmod +x build_thirdparty.sh       
./build_thirdparty.sh

  • Complie the Active SLAM and YOLO:

    cd {ROS_WORKSPACE}
catkin_make

3. Usage of view planning method

There are many parameters in the file "config/kinectv1.yaml" that can affect VP-SOM. This section will introduce these parameters.

  • [PubGlobalGoal] :
  • [PubLocalGoal] :
  • [MAM.Reward_angle_cost] :
  • [MAM.Reward_dis] :
  • [Planle.Safe_radius] :
  • [ConstraintType] :
  • [ObserveMaxNumBackgroudObject] :
  • [IE.ThresholdEndMapping] :
  • [Plane.Height.Max] and [Plane.Height.Min] :
  • [IE.PublishIEwheel] :
  • [IE.P_occ], [IE.P_free], [IE.P_prior] :
  • [IE.ThresholdPointNum] :
  • Series of [Trobot_camera] :
  • Series of [Tworld_camer] :
  • Other parameters have little effect and will be updated in the future.

4. Simulization Environment and Fabo robot

    1. Simulization Environment

    • One backgroud object

      roslaunch ASLAM_gazebo_world hokuyo_kinectv1_bringup_moveit_1bo.launch

    • Two backgroud object

      roslaunch ASLAM_gazebo_world hokuyo_kinectv1_bringup_moveit_2bo.launch

    • Three backgroud object

      roslaunch ASLAM_gazebo_world hokuyo_kinectv1_bringup_moveit_3bo.launch

      Figure 1

    1. Fabo robot controller

    • Control robot by keyboard. This corresponds to manual mode where PubGlobalGoal=0. Press "IJLK," to control the movement of the chassis. Press "G" to publish a signal that has arrived at the NBV to the view-planning program to start a new round of view-planning

      roslaunch fabo_teleop fabo_teleop.launch

    • Robot move autonomously by MoveIt and 2D grid map. This corresponds to autonomous mode where PubGlobalGoal=1.

      roslaunch fabo_robot_gazebo  fake_navigation.launch

5. Test the Active SLAM

    1. Start simulation environment and robot controller as the section 4
    1. YOLO object detection
    roslaunch darknet_ros darknet_kinectv1.launch
    1. Active SLAM
    roslaunch active_slam_vpsom aslam.launch
    • Visulization of object map, camera view and robot motion roslaunch active_slam_vpsom rviz.launch

Figure 2

6. Evaluation

The results of sparse object map and observation trajectories of different view-planning methods are saved in "eval/temp". Evaluate various methods by comparing sparse object maps and observation trajectories.

rosrun active_slam_vpsom eval

The groudtruth of object models in the simulation environments can be extracted from the "world" file of gazebo

rosrun active_eao_new extract_gazebo_groudth  [the path of gazebo world file]

Other details to be updated later.