Hi everyone,

Please give me some advices for this problem with ghost obstacles.

My mobile robot is working with ROS (Kinetic, Ubuntu 16.0.4) using Sick sensor. My robot uses 2 Sick sensor, one is in the right-upper corner and another one is in left-bottom corner. I use ira_laser_tools to merge data of these 2 laser scanners.

However, I am stucking at one problem: ghost obstacles appear on the costmap, and this ghost obstacle is relative to my mobile robot. If my robot moves, the ghost obstacle relatively follow. It causes mobile robot try to avoid obstacles even though there is no real obstacles.

I have tried to use some different laser-filter packages, but this problem still happen.

Can you please give me some suggestion ?

Thanks in advances

I took a video about my error (ghost obstacle) as in the link below:

https://www.youtube.com/watch?v=THt7o...

And, the picture within the indication for ghost obstacles here:

And, the costmap configuration:

costmap_common_params:

footprint: [[0.44, 0.34], [0.44, -0.34], [-0.44, -0.34], [-0.44, 0.34] ]

footprint_padding: 0.0

transform_tolerance: 0.2
map_type: costmap

obstacle_layer:
 enabled: true
 obstacle_range: 1.5
 raytrace_range: 3.0
 inflation_radius: 0.2
 track_unknown_space: true
 combination_method: 1
 min_obstacle_height: 0.1
 max_obstacle_height: 0.5

 observation_sources: laser_scan_sensor
 laser_scan_sensor: {data_type: LaserScan, topic: scan, marking: true, clearing: true}


inflation_layer:
  enabled:              true
  cost_scaling_factor:  10.0  # exponential rate at which the obstacle cost drops off (default: 10)
  inflation_radius:     0.6 # max. distance from an obstacle at which costs are incurred for planning paths.

static_layer:
  enabled:              true
  map_topic:            "map"

rgbd_obstacle_layer:

  enabled:               true

  voxel_decay:           0.4     #seconds if linear, e^n if exponential

  decay_model:           0      #0=linear, 1=exponential, -1=persistent

  voxel_size:            0.05   #meters

  track_unknown_space:   true   #default space is unknown

  observation_persistence: 0.0  #seconds

  max_obstacle_height:   2.0    #meters

  unknown_threshold:     15     #voxel height

  mark_threshold:        0      #voxel height

  update_footprint_enabled: true

  combination_method:    1      #1=max, 0=override

  obstacle_range:        2.0    #meters

  origin_z:              0.0    #meters

  publish_voxel_map:     true   # default off

  transform_tolerance:   0.2    # seconds

  mapping_mode:          false  # default off, saves map not for navigation

  map_save_duration:     60     #default 60s, how often to autosave

  observation_sources:   rgbd1_clear rgbd1_mark rgbd2_clear rgbd2_mark

  rgbd1_mark:

    data_type: PointCloud2

    topic: cam_1/depth/color/points

    marking: true

    clearing: false

    min_obstacle_height: 0.15     #default 0, meters

    max_obstacle_height: 2.0     #defaule 3, meters

    expected_update_rate: 0    #default 0, if not updating at this rate at least, remove from buffer

    observation_persistence: 0.0 #default 0, use all measurements taken during now-value, 0=latest 

    inf_is_valid: false          #default false, for laser scans

    clear_after_reading: true    #default false, clear the buffer after the layer gets readings from it

    voxel_filter: true           #default off, apply voxel filter to sensor, recommend on 

  rgbd1_clear:

 data_type: PointCloud2

    topic: cam_1/depth/color/points

    marking: false

    clearing: true

    min_z: 0.1                   #default 0, meters

    max_z: 7.0                   #default 10, meters

    vertical_fov_angle: 0.7      #default 0.7, radians

    horizontal_fov_angle: 1.04   #default 1.04, radians

    decay_acceleration: 1.       #default 0, 1/s^2. If laser scanner MUST be 0

    model_type: 0                #default 0 (depth camera). Use 1 for 3D Lida ...