ObstacleLayer and VoxelLayer don't generate obstacles

edit

Hello everyone.

I have the issue as i said in the title that ObstacleLayer and VoxelLayer don't generate obstacle. I am using a Realsense camera, model D435 to generate a pointcloud and then using the depthimage_to_laserscan package transform the depth image topic to laser scan topic. It seems to work.

Then I have configured the costmap package as you can see below:

local_costmap_params.yaml

local_costmap:
  plugins:
    - {name: laser_layer, type: "costmap_2d::ObstacleLayer"} #Laser sensors
    - {name: pointcloud_layer, type: "costmap_2d::VoxelLayer"} #Laser sensors
    - {name: static_map,       type: "costmap_2d::StaticLayer"}
    - {name: inflation_layer,  type: "costmap_2d::InflationLayer"}
    #- {name: ultrasonic,       type: "range_sensor_layer::RangeSensorLayer"}

  update_frequency: 2.0 #HIGH CPU usage with sensors
  publish_frequency: 50.0

  global_frame: "odom" #To inflate obstacles
  robot_base_frame: "base_link"

  #static_map: false
  rolling_window: true
  width: 6.0 #6
  height: 6.0 #6
  resolution: 0.05 #0.01

global_costmap_params.yaml

global_costmap:

  plugins:
    - {name: static_map,       type: "costmap_2d::StaticLayer"}
    - {name: inflation_layer, type: "costmap_2d::InflationLayer"}
    #- {name: ultrasonic,   type: "range_sensor_layer::RangeSensorLayer"}


  global_frame: "map"
  robot_base_frame: "base_link"

  publish_frequency: 50.0
  update_frequency: 2.0

  resolution: 0.5 #0.01 #The resolution of the map in meters/cell.
  transform_tolerance: 0.2 #Specifies the delay in transform (tf) data that is tolerable in seconds
  map_type: costmap

costmap_common_params.yaml

footprint: [[-0.30 , 0.38], [0.70, 0.38], [0.70, -0.38], [-0.30, -0.38]]

laser_layer: #Laser
  enabled: true
  obstacle_range: 2.0
  raytrace_range: 4.0
  max_obstacle_height: 2.0
  inflation_radius: 0.7
  combination_method: 1
  observation_sources:  scan
  scan: {data_type: LaserScan, topic: /scan, marking: true, clearing: true, expected_update_rate: 0, sensor_frame: camera_depth_frame}

pointcloud_layer: #Nube de puntos
  enabled: true
  obstacle_range: 2.0
  raytrace_range: 4.0
  inflation_radius: 0.7
  observation_sources: pointcloud
  pointcloud: {data_type: PointCloud2, topic: /camera/depth/color/points, marking: true, clearing: true, expected_update_rate: 0}


inflation_layer:
  enabled: true
  inflation_radius: 0.75

base_local_planner_params.yaml

#recovery_behavior_enabled: false
#clearing_rotation_allowed: false
controller_frequency: 10 #Default 20 took many time

TrajectoryPlannerROS:

  max_vel_x: 0.4 #meters/sec #0.6
  min_vel_x: -0.1
  max_vel_y: 0.0  # zero for a differential drive robot
  min_vel_y: 0.0  #radians/sec
  max_vel_theta: 1.0 #3
  min_vel_theta: -1.0
  min_in_place_vel_theta: 0.1 #radians/sec, in-place rotations
  escape_vel: -0.1 #0.1
  acc_lim_x: 0.4 #meters/sec^2
  acc_lim_y: 0.0  # zero for a differential drive robot
  acc_lim_theta: 1.0 #radians/sec^2

  holonomic_robot: false

   #####Trajectory Scoring Parameters#####

  meter_scoring: true #goal_distance and path_distance are expressed in units of meters or cells. Cells false.
  #pdist_scale: 0.4 #The weighting for how much the controller should stay close to the path it was given
  #gdist_scale: 0.8 #The weighting for how much the controller should attempt to reach its local goal, also controls speed

  yaw_goal_tolerance: 0.5 # about 30 degrees, The tolerance in radians for the controller in yaw/rotation when achieving its goal
  xy_goal_tolerance: 0.20  # 5 cm, The tolerance in meters for the controller in the x & y distance when achieving a goal
   #latch_xy_goal_tolerance: false


   #heading_lookahead: 0.325 #How far to look ahead in meters when scoring different in-place-rotation trajectories
   #heading_scoring: false #Whether to score based on the robot's heading to the path or its distance from the path
   #heading_scoring_timestep: 0.8 #How far ...