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TINY-KE / VP-SOM2

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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.

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