In this section we introduce a new file, config/nav2_params.yaml, that contains the configuration settings for our bot. This file is used by nav2_costmap_2d. In our example, we will use static layer, obstacle layer, voxel layer, and inflation layer. Both obstacle and voxel layers will use use the LaserScan messages published to the /scan topic by the lidar sensor.
We will also set some parameters to define how the detected obstacles are reflected in the costmap. For the complete list of layer plugin parameters, see the Costmap 2D Configuration Guide.
config/nav2_params.yaml:
amcl:
ros__parameters:
set_initial_pose: true
initial_pose:
x: 0.0
y: 0.0
z: 0.0
yaw: 0.0
use_sim_time: true
alpha1: 0.002
alpha2: 0.002
alpha3: 0.002
alpha4: 0.002
alpha5: 0.002
base_frame_id: "base_footprint"
beam_skip_distance: 0.5
beam_skip_error_threshold: 0.9
beam_skip_threshold: 0.3
do_beamskip: false
global_frame_id: "map"
initial_pose: [0.0, 0.0, 0.0, 0.0]
lambda_short: 0.1
laser_likelihood_max_dist: 2.0
laser_max_range: 100.0
laser_min_range: -1.0
laser_model_type: "likelihood_field"
max_beams: 60
max_particles: 2000
min_particles: 500
odom_frame_id: "odom"
pf_err: 0.05
pf_z: 0.99
recovery_alpha_fast: 0.0
recovery_alpha_slow: 0.0
resample_interval: 1
robot_model_type: "differential"
save_pose_rate: 0.5
set_initial_pose: false
sigma_hit: 0.2
tf_broadcast: true
transform_tolerance: 1.0
update_min_a: 0.2
update_min_d: 0.25
z_hit: 0.5
z_max: 0.05
z_rand: 0.5
z_short: 0.05
scan_topic: scan
amcl_map_client:
ros__parameters:
use_sim_time: true
amcl_rclcpp_node:
ros__parameters:
use_sim_time: true
bt_navigator:
ros__parameters:
use_sim_time: true
global_frame: map
robot_base_frame: base_link
odom_topic: /odometry/filtered
enable_groot_monitoring: true
groot_zmq_publisher_port: 1666
groot_zmq_server_port: 1667
default_bt_xml_filename: "navigate_w_replanning_and_recovery.xml"
plugin_lib_names:
- nav2_compute_path_to_pose_action_bt_node
- nav2_follow_path_action_bt_node
- nav2_back_up_action_bt_node
- nav2_spin_action_bt_node
- nav2_wait_action_bt_node
- nav2_clear_costmap_service_bt_node
- nav2_is_stuck_condition_bt_node
- nav2_goal_reached_condition_bt_node
- nav2_goal_updated_condition_bt_node
- nav2_initial_pose_received_condition_bt_node
- nav2_reinitialize_global_localization_service_bt_node
- nav2_rate_controller_bt_node
- nav2_distance_controller_bt_node
- nav2_speed_controller_bt_node
- nav2_truncate_path_action_bt_node
- nav2_goal_updater_node_bt_node
- nav2_recovery_node_bt_node
- nav2_pipeline_sequence_bt_node
- nav2_round_robin_node_bt_node
- nav2_transform_available_condition_bt_node
- nav2_time_expired_condition_bt_node
- nav2_distance_traveled_condition_bt_node
bt_navigator_rclcpp_node:
ros__parameters:
use_sim_time: true
controller_server:
ros__parameters:
use_sim_time: true
controller_frequency: 2.0
min_x_velocity_threshold: 0.001
min_y_velocity_threshold: 0.5
min_theta_velocity_threshold: 0.001
progress_checker_plugin: "progress_checker"
goal_checker_plugin: "goal_checker"
controller_plugins: ["FollowPath"]
# Progress checker parameters
progress_checker:
plugin: "nav2_controller::SimpleProgressChecker"
required_movement_radius: 0.5
movement_time_allowance: 10.0
# Goal checker parameters
goal_checker:
plugin: "nav2_controller::SimpleGoalChecker"
xy_goal_tolerance: 0.25
yaw_goal_tolerance: 0.25
stateful: true
# DWB parameters
FollowPath:
plugin: "dwb_core::DWBLocalPlanner"
debug_trajectory_details: true
min_vel_x: 0.0
min_vel_y: 0.0
# Default 0.26 for max_vel_x and max_speed_xy and 1.0 for max_vel_theta
max_vel_x: 0.26
max_vel_y: 0.0
max_vel_theta: 1.0
min_speed_xy: 0.0
max_speed_xy: 0.26
min_speed_theta: 0.0
# Add high threshold velocity for turtlebot 3 issue.
# https://github.com/ROBOTIS-GIT/turtlebot3_simulations/issues/75
acc_lim_x: 2.5
acc_lim_y: 0.0
acc_lim_theta: 3.2
decel_lim_x: -2.5
decel_lim_y: 0.0
decel_lim_theta: -3.2
vx_samples: 20
vy_samples: 5
vtheta_samples: 20
sim_time: 1.7
linear_granularity: 0.05
angular_granularity: 0.025
transform_tolerance: 0.2
xy_goal_tolerance: 0.25
trans_stopped_velocity: 0.25
short_circuit_trajectory_evaluation: true
stateful: true
critics: ["RotateToGoal", "Oscillation", "BaseObstacle", "GoalAlign", "PathAlign", "PathDist", "GoalDist"]
BaseObstacle.scale: 0.02
PathAlign.scale: 32.0
PathAlign.forward_point_distance: 0.1
GoalAlign.scale: 24.0
GoalAlign.forward_point_distance: 0.1
PathDist.scale: 32.0
GoalDist.scale: 24.0
RotateToGoal.scale: 32.0
RotateToGoal.slowing_factor: 5.0
RotateToGoal.lookahead_time: -1.0
controller_server_rclcpp_node:
ros__parameters:
use_sim_time: true
local_costmap:
local_costmap:
ros__parameters:
update_frequency: 5.0
publish_frequency: 2.0
global_frame: odom
robot_base_frame: base_link
use_sim_time: true
rolling_window: true
width: 10
height: 10
resolution: 0.05
robot_radius: 0.75
plugins: ["voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 3.0
inflation_radius: 0.95
voxel_layer:
plugin: "nav2_costmap_2d::VoxelLayer"
enabled: true
publish_voxel_map: true
origin_z: 0.0
z_resolution: 0.05
z_voxels: 16
max_obstacle_height: 2.0
mark_threshold: 0
observation_sources: scan
scan:
topic: /scan
max_obstacle_height: 2.0
clearing: true
marking: true
data_type: "LaserScan"
static_layer:
map_subscribe_transient_local: true
always_send_full_costmap: true
local_costmap_client:
ros__parameters:
use_sim_time: true
local_costmap_rclcpp_node:
ros__parameters:
use_sim_time: true
global_costmap:
global_costmap:
ros__parameters:
update_frequency: 1.0
publish_frequency: 1.0
global_frame: map
robot_base_frame: base_link
use_sim_time: true
robot_radius: 0.75
resolution: 0.05
track_unknown_space: true
plugins: ["static_layer", "obstacle_layer", "inflation_layer"]
obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: true
observation_sources: scan
scan:
topic: /scan
max_obstacle_height: 2.0
clearing: true
marking: true
data_type: "LaserScan"
static_layer:
plugin: "nav2_costmap_2d::StaticLayer"
map_subscribe_transient_local: true
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 3.0
inflation_radius: 0.95
always_send_full_costmap: true
global_costmap_client:
ros__parameters:
use_sim_time: true
global_costmap_rclcpp_node:
ros__parameters:
use_sim_time: true
map_server:
ros__parameters:
use_sim_time: true
yaml_filename: "map.yaml"
topic_name: "map"
frame_id: "map"
map_saver:
ros__parameters:
use_sim_time: true
save_map_timeout: 5000
free_thresh_default: 0.25
occupied_thresh_default: 0.65
map_subscribe_transient_local: false
planner_server:
ros__parameters:
expected_planner_frequency: 2.0
use_sim_time: true
planner_plugins: ["GridBased"]
GridBased:
plugin: "nav2_navfn_planner/NavfnPlanner"
tolerance: 0.5
use_astar: false
allow_unknown: true
costmap_filter_info_server:
ros__parameters:
type: 1
filter_info_topic: "costmap_filter_info"
mask_topic: "filter_mask"
base: 0.0
multiplier: 0.25
planner_server_rclcpp_node:
ros__parameters:
use_sim_time: true
recoveries_server:
ros__parameters:
costmap_topic: local_costmap/costmap_raw
footprint_topic: local_costmap/published_footprint
cycle_frequency: 10.0
recovery_plugins: ["spin", "back_up", "wait"]
spin:
plugin: "nav2_recoveries/Spin"
back_up:
plugin: "nav2_recoveries/BackUp"
wait:
plugin: "nav2_recoveries/Wait"
global_frame: odom
robot_base_frame: base_link
transform_timeout: 0.1
use_sim_time: true
simulate_ahead_time: 2.0
max_rotational_vel: 1.0
min_rotational_vel: 0.4
rotational_acc_lim: 3.2
robot_state_publisher:
ros__parameters:
use_sim_time: true
slam_toolbox:
ros__parameters:
# Plugin params
solver_plugin: solver_plugins::CeresSolver
ceres_linear_solver: SPARSE_NORMAL_CHOLESKY
ceres_preconditioner: SCHUR_JACOBI
ceres_trust_strategy: LEVENBERG_MARQUARDT
ceres_dogleg_type: TRADITIONAL_DOGLEG
ceres_loss_function: None
# ROS Parameters
odom_frame: odom
map_frame: map
base_frame: base_footprint
scan_topic: /scan
mode: mapping #localization
# if you'd like to immediately start continuing a map at a given pose
# or at the dock, but they are mutually exclusive, if pose is given
# will use pose
#map_file_name: test_steve
#map_start_pose: [0.0, 0.0, 0.0]
#map_start_at_dock: true
debug_logging: false
throttle_scans: 1
transform_publish_period: 0.02 #if 0 never publishes odometry
map_update_interval: 5.0
resolution: 0.05
max_laser_range: 20.0 #for rastering images
minimum_time_interval: 0.5
transform_timeout: 0.2
tf_buffer_duration: 30.
stack_size_to_use: 40000000 #// program needs a larger stack size to serialize large maps
enable_interactive_mode: true
# General Parameters
use_scan_matching: true
use_scan_barycenter: true
minimum_travel_distance: 0.5
minimum_travel_heading: 0.5
scan_buffer_size: 10
scan_buffer_maximum_scan_distance: 10.0
link_match_minimum_response_fine: 0.1
link_scan_maximum_distance: 1.5
loop_search_maximum_distance: 3.0
do_loop_closing: true
loop_match_minimum_chain_size: 10
loop_match_maximum_variance_coarse: 3.0
loop_match_minimum_response_coarse: 0.35
loop_match_minimum_response_fine: 0.45
# Correlation Parameters - Correlation Parameters
correlation_search_space_dimension: 0.5
correlation_search_space_resolution: 0.01
correlation_search_space_smear_deviation: 0.1
# Correlation Parameters - Loop Closure Parameters
loop_search_space_dimension: 8.0
loop_search_space_resolution: 0.05
loop_search_space_smear_deviation: 0.03
# Scan Matcher Parameters
distance_variance_penalty: 0.5
angle_variance_penalty: 1.0
fine_search_angle_offset: 0.00349
coarse_search_angle_offset: 0.349
coarse_angle_resolution: 0.0349
minimum_angle_penalty: 0.9
minimum_distance_penalty: 0.5
use_response_expansion: true
waypoint_follower:
ros__parameters:
loop_rate: 1
stop_on_failure: false
lifecycle_manager:
ros__parameters:
autostart: true
node_names: ['nav2_map_server', 'controller_server', 'planner_server', 'behavior_server', 'bt_navigator', 'waypoint_follower']
bond_timeout: 4.0
attempt_respawn_reconnection: true
bond_respawn_max_duration: 10.0
Notice that we have two two different costmaps here: global_costmap and local_costmap. The global_costmap is mainly used for long-term planning over the whole map while local_costmap is for short-term planning and collision avoidance.
The layers that we want to use are defined in the plugins parameter, both for global_costmap and local_costmap:
local_costmap:
...
plugins: ["voxel_layer", "inflation_layer"]
These values list the mapped layer names, and also serve as namespaces for the layer parameters:
plugins: ["voxel_layer", "inflation_layer"]
inflation_layer:
...
voxel_layer:
...
Note that each layer/namespace in this list must have a plugin parameter defining the type of plugin to be loaded for that specific layer.
For the static layer, we set the map_subscribe_transient_local parameter to True. This sets the QoS settings for the map topic.
Another important parameter for the static layer is the map_topic which defines the map topic to subscribe to. This defaults to /map topic when not defined.
For the obstacle layer, we define its sensor source under the observation_sources parameter as scan (and on the following lines, we set its parameters). We set its topic parameter as the topic that publishes the defined sensor source and we set the data_type according to the sensor source it will use. In our configuration, the obstacle layer will use the LaserScan published by the lidar sensor to /scan.
Note that the obstacle layer and voxel layer can use either or both LaserScan and PointCloud2 as their data_type but it is set to LaserScan by default. The code snippet below shows an example of using both the LaserScan and PointCloud2 as the sensor sources. This may be particularly useful when setting up your own physical robot.
obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan pointcloud
scan:
topic: /scan
data_type: "LaserScan"
pointcloud:
topic: /depth_camera/points
data_type: "PointCloud2"
For the other parameters of the obstacle layer, the max_obstacle_height parameter sets the maximum height of the sensor reading to return to the occupancy grid. The minimum height of the sensor reading can also be set using the min_obstacle_height parameter, which defaults to 0 since we did not set it in the configation.
The clearing parameter is used to set whether the obstacle is to be removed from the costmap or not. The clearing operation is done by raytracing through the grid.
The maximum and minimum range to raytrace clear objects from the costmap is set using the raytrace_max_range and raytrace_min_range respectively.
The marking parameter is used to set whether the inserted obstacle is marked into the costmap or not.
We also set the maximum and minimum range to mark obstacles in the costmap through the obstacle_max_range and obstacle_min_range respectively.
For the inflation layer, we set the exponential decay factor across the inflation radius using the cost_scaling_factor parameter. The value of the radius to inflate around lethal obstacles is defined using the inflation_radius.
For the voxel layer, we set the publish_voxel_map parameter to True to enable the publishing of the 3D voxel grid. The resolution of the voxels in height is defined using the z_resolution parameter, while the number of voxels in each column is defined using the z_voxels parameter.
The mark_threshold parameter sets the minimum number of voxels in a column to mark as occupied in the occupancy grid. We set the observation_sources parameter of the voxel layer to scan, and we set the scan parameters similar to the parameters that we have discussed for the obstacle layer. As defined in its topic and data_type parameters, the voxel layer will use the LaserScan published on the /scan topic by the lidar scanner.
Note that the we are not using a range layer for our configuration but it may be useful for your own robot setup. For the range layer, its basic parameters are the topics, input_sensor_type, and clear_on_max_reading parameters. The range topics to subscribe to are defined in the topics parameter. The input_sensor_type is set to either ALL, VARIABLE, or FIXED. The clear_on_max_reading is a boolean parameter that sets whether to clear the sensor readings on max range.