Small obstacles and holes detection

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I'm beginner, I'm using the default navigation params of Turtlebot and wondering what parameters should be changed to obtain the desired map. the common costmap

    max_obstacle_height: 0.60  # assume something like an arm is mounted on top of the robot

# Obstacle Cost Shaping (http://wiki.ros.org/costmap_2d/hydro/inflation)
robot_radius: 0.20  # distance a circular robot should be clear of the obstacle (kobuki: 0.18)
# footprint: [[x0, y0], [x1, y1], ... [xn, yn]]  # if the robot is not circular

map_type: voxel

obstacle_layer:
  enabled:              true
  max_obstacle_height:  0.6
  origin_z:             0.0
  z_resolution:         0.2
  z_voxels:             2C
  unknown_threshold:    15
  mark_threshold:       0
  combination_method:   1
  track_unknown_space:  false    #true needed for disabling global path planning through unknown space
  obstacle_range: 2.5
  raytrace_range: 3.0
  origin_z: 0.0
  z_resolution: 0.2
  z_voxels: 2
  publish_voxel_map: false

#BEGIN VOXEL STUFF

  observation_sources:  scan bump

  #observation_sources:  base_scan_marking base_scan tilt_scan #ground_object_cloud

  scan:
    data_type: LaserScan
    topic: scan
    marking: true
    clearing: true
    min_obstacle_height: 0.0
    max_obstacle_height: 3

 bump:
    data_type: PointCloud2
    topic: mobile_base/sensors/bumper_pointcloud
    marking: true
    clearing: false
    min_obstacle_height: 0.0
    max_obstacle_height: 3

#base_scan_marking: {sensor_frame: base_laser_link, topic: /#base_scan_marking, data_type: PointCloud2, #expected_update_rate: 0.2,
  #observation_persistence: 0.0, marking: true, clearing: false, #min_obstacle_height: 0.08, max_obstacle_height: 2.0}

#base_scan: {sensor_frame: base_laser_link, topic: /base_scan, #data_type: LaserScan, expected_update_rate: 0.2,
             #observation_persistence: 0.0, marking: false, #clearing: true, min_obstacle_height: -0.10, #max_obstacle_height: 2.0}

#tilt_scan: {sensor_frame: laser_tilt_link, topic: /#tilt_scan_interpolated, data_type: LaserScan, #expected_update_rate: 0.2,
             #observation_persistence: 0.2, marking: false, #clearing: true, min_obstacle_height: -20.00, #max_obstacle_height: 40.0}

#cost_scaling_factor and inflation_radius were now moved to the inflation_layer ns
inflation_layer:
  enabled:              true
  cost_scaling_factor:  5.0  # exponential rate at which the obstacle cost drops off (default: 10)
  inflation_radius:     0.5  # max. distance from an obstacle at which costs are incurred for planning paths.

static_layer:
  enabled:              true

here is the r200 costmap param yaml file

global_costmap:
  robot_radius: 0.20  # distance a circular robot should be clear of the obstacle (kobuki: 0.18)
  obstacle_layer:
    scan:
      data_type: LaserScan
      topic: scan
      marking: true
      clearing: true
      min_obstacle_height: 0.05 # previous: 0.25, too high for the R200 configuration!
      max_obstacle_height: 0.35

local_costmap:
  robot_radius: 0.18  # distance a circular robot should be clear of the obstacle (kobuki: 0.18)
  obstacle_layer:
    scan:
      data_type: LaserScan
      topic: scan
      marking: true
      clearing: true
      min_obstacle_height: 0.05 # previous: 0.25, too high for the R200 configuration!
      max_obstacle_height: 0.35