# Example:
#
#   Bringup turtlebot3:
#     $ export TURTLEBOT3_MODEL=waffle
#     $ export LDS_MODEL=LDS-01
#     $ ros2 launch turtlebot3_bringup robot.launch.py
#
#   SLAM:
#     $ ros2 launch rtabmap_demos turtlebot3_rgbd_scan.launch.py
#
#   Navigation (install nav2_bringup package):
#     $ ros2 launch nav2_bringup navigation_launch.py
#     $ ros2 launch nav2_bringup rviz_launch.py
#
#   Teleop:
#     $ ros2 run turtlebot3_teleop teleop_keyboard

from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, SetEnvironmentVariable
from launch.substitutions import LaunchConfiguration
from launch.conditions import IfCondition, UnlessCondition
from launch_ros.actions import Node


def generate_launch_description():

    use_sim_time = LaunchConfiguration('use_sim_time')
    localization = LaunchConfiguration('localization')

    parameters={
          'frame_id':'base_footprint',
          'use_sim_time':use_sim_time,
          'subscribe_rgbd':True,
          'subscribe_scan':True,
          'use_action_for_goal':True,
          # RTAB-Map's parameters should be strings:
          'Reg/Strategy':'1',
          'Reg/Force3DoF':'true',
          'RGBD/NeighborLinkRefining':'True',
          'Grid/RayTracing':'true', # Fill empty space
          'Grid/3D':'false', # Use 2D occupancy
          'Grid/RangeMax':'3',
          'Grid/NormalsSegmentation':'false', # Use passthrough filter to detect obstacles
          'Grid/Sensor':'2', # Use both laser scan and camera for obstacle detection in global map
          'Grid/MaxGroundHeight':'0.05', # All points above 5 cm are obstacles
          'Grid/MaxObstacleHeight':'0.4',  # All points over 1 meter are ignored
          'Grid/RangeMin':'0.2', # ignore laser scan points on the robot itself
          'Optimizer/GravitySigma':'0' # Disable imu constraints (we are already in 2D)
    }

    remappings=[
          ('rgb/image', '/camera/image_raw'),
          ('rgb/camera_info', '/camera/camera_info'),
          ('depth/image', '/camera/depth/image_raw')]

    return LaunchDescription([

        # Launch arguments
        DeclareLaunchArgument(
            'use_sim_time', default_value='false',
            description='Use simulation (Gazebo) clock if true'),

        DeclareLaunchArgument(
            'localization', default_value='false',
            description='Launch in localization mode.'),

        # Nodes to launch
        Node(
            package='rtabmap_sync', executable='rgbd_sync', output='screen',
            parameters=[{'approx_sync':False, 'use_sim_time':use_sim_time}],
            remappings=remappings),

        # SLAM Mode:
        Node(
            condition=UnlessCondition(localization),
            package='rtabmap_slam', executable='rtabmap', output='screen',
            parameters=[parameters],
            remappings=remappings,
            arguments=['-d']),
            
        # Localization mode:
        Node(
            condition=IfCondition(localization),
            package='rtabmap_slam', executable='rtabmap', output='screen',
            parameters=[parameters,
              {'Mem/IncrementalMemory':'False',
               'Mem/InitWMWithAllNodes':'True'}],
            remappings=remappings),

        Node(
            package='rtabmap_viz', executable='rtabmap_viz', output='screen',
            parameters=[parameters],
            remappings=remappings),
        
        # Obstacle detection with the camera for nav2 local costmap.
        # First, we need to convert depth image to a point cloud.
        # Second, we segment the floor from the obstacles.
        Node(
            package='rtabmap_util', executable='point_cloud_xyz', output='screen',
            parameters=[{'decimation': 2,
                         'max_depth': 3.0,
                         'voxel_size': 0.02}],
            remappings=[('depth/image', '/camera/depth/image_raw'),
                        ('depth/camera_info', '/camera/camera_info'),
                        ('cloud', '/camera/cloud')]),
        Node(
            package='rtabmap_util', executable='obstacles_detection', output='screen',
            parameters=[parameters],
            remappings=[('cloud', '/camera/cloud'),
                        ('obstacles', '/camera/obstacles'),
                        ('ground', '/camera/ground')]),
    ])