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Go2Py_SIM
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exploration parameters are updated

This commit is contained in:
Rooholla-KhorramBakht 2024-07-22 11:56:47 +08:00
parent 36090b407a
commit b7d6503816
6 changed files with 491 additions and 75 deletions
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1
deploy/docker/Dockerfile.nav2
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@ -15,6 +15,7 @@ RUN apt-get update && apt-get install -y \
ros-humble-slam-toolbox \
ros-humble-robot-localization\
ros-humble-image-geometry \
ros-humble-test-msgs \
&& rm -rf /var/lib/apt/lists/*
# Cheange the ROS2 RMW to CycloneDDS as instructed by Unitree

4
deploy/ros2_nodes/lidar_node/src/manager/source_driver_ros2.hpp
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@ -299,9 +299,9 @@ inline void SourceDriver::publishLaserScan(
scan_msg->time_increment = 0.0;
scan_msg->scan_time = 0.3333;
scan_msg->range_min = 0.1;
scan_msg->range_max = 15;
scan_msg->range_max = 50;
float max_height_ = 1.0;
float min_height_ = -0.1;
float min_height_ = -0.05;
// scan_msg->angle_min = -(3*3.1415)/4;
// scan_msg->angle_max = -3.1415/4;

4
deploy/ros2_nodes/sportmode_nav2/params/mapping_async.yaml
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@ -28,8 +28,8 @@ slam_toolbox:
transform_publish_period: 0.02 #if 0 never publishes odometry
map_update_interval: 5.0
resolution: 0.05
max_laser_range: 5.0 #for rastering images
minimum_time_interval: 0.5
max_laser_range: 8.0 #for rastering images
minimum_time_interval: 0.1
transform_timeout: 0.2
tf_buffer_duration: 30.
stack_size_to_use: 40000000 #// program needs a larger stack size to serialize large maps

350
deploy/ros2_nodes/sportmode_nav2/params/nav2_params.old.yaml Normal file
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@ -0,0 +1,350 @@
amcl:
ros__parameters:
use_sim_time: False
alpha1: 0.2
alpha2: 0.2
alpha3: 0.2
alpha4: 0.2
alpha5: 0.2
base_frame_id: "base_link"
beam_skip_distance: 0.5
beam_skip_error_threshold: 0.9
beam_skip_threshold: 0.3
do_beamskip: false
global_frame_id: "map"
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: "nav2_amcl::DifferentialMotionModel"
save_pose_rate: 0.5
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: /go2/scan
bt_navigator:
ros__parameters:
use_sim_time: False
global_frame: map
robot_base_frame: base_link
odom_topic: /go2/odom
bt_loop_duration: 10
default_server_timeout: 20
# 'default_nav_through_poses_bt_xml' and 'default_nav_to_pose_bt_xml' are use defaults:
# nav2_bt_navigator/navigate_to_pose_w_replanning_and_recovery.xml
# nav2_bt_navigator/navigate_through_poses_w_replanning_and_recovery.xml
# They can be set here or via a RewrittenYaml remap from a parent launch file to Nav2.
plugin_lib_names:
- nav2_compute_path_to_pose_action_bt_node
- nav2_compute_path_through_poses_action_bt_node
- nav2_smooth_path_action_bt_node
- nav2_follow_path_action_bt_node
- nav2_spin_action_bt_node
- nav2_wait_action_bt_node
- nav2_assisted_teleop_action_bt_node
- nav2_back_up_action_bt_node
- nav2_drive_on_heading_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_globally_updated_goal_condition_bt_node
- nav2_is_path_valid_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_truncate_path_local_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_path_expiring_timer_condition
- nav2_distance_traveled_condition_bt_node
- nav2_single_trigger_bt_node
- nav2_goal_updated_controller_bt_node
- nav2_is_battery_low_condition_bt_node
- nav2_navigate_through_poses_action_bt_node
- nav2_navigate_to_pose_action_bt_node
- nav2_remove_passed_goals_action_bt_node
- nav2_planner_selector_bt_node
- nav2_controller_selector_bt_node
- nav2_goal_checker_selector_bt_node
- nav2_controller_cancel_bt_node
- nav2_path_longer_on_approach_bt_node
- nav2_wait_cancel_bt_node
- nav2_spin_cancel_bt_node
- nav2_back_up_cancel_bt_node
- nav2_assisted_teleop_cancel_bt_node
- nav2_drive_on_heading_cancel_bt_node
- nav2_is_battery_charging_condition_bt_node
bt_navigator_navigate_through_poses_rclcpp_node:
ros__parameters:
use_sim_time: False
bt_navigator_navigate_to_pose_rclcpp_node:
ros__parameters:
use_sim_time: False
controller_server:
ros__parameters:
use_sim_time: False
controller_frequency: 20.0
min_x_velocity_threshold: 0.001
min_y_velocity_threshold: 0.5
min_theta_velocity_threshold: 0.001
failure_tolerance: 0.3
progress_checker_plugin: "progress_checker"
goal_checker_plugins: ["general_goal_checker"] # "precise_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
#precise_goal_checker:
# plugin: "nav2_controller::SimpleGoalChecker"
# xy_goal_tolerance: 0.25
# yaw_goal_tolerance: 0.25
# stateful: True
general_goal_checker:
stateful: True
plugin: "nav2_controller::SimpleGoalChecker"
xy_goal_tolerance: 0.5
yaw_goal_tolerance: 1.
# DWB parameters
FollowPath:
plugin: "dwb_core::DWBLocalPlanner"
debug_trajectory_details: True
min_vel_x: 0.0
min_vel_y: 0.0
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
local_costmap:
local_costmap:
ros__parameters:
update_frequency: 5.0
publish_frequency: 2.0
global_frame: map
robot_base_frame: base_link
use_sim_time: False
rolling_window: true
width: 3
height: 3
resolution: 0.05
# robot_radius: 0.22
footprint: "[ [0.35, 0.195], [0.35, -0.195], [-0.35, -0.195], [-0.35, 0.195] ]"
plugins: ["voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 3.0
inflation_radius: 0.2
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: /go2/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
raytrace_max_range: 3.0
raytrace_min_range: 0.0
obstacle_max_range: 2.5
obstacle_min_range: 0.0
static_layer:
plugin: "nav2_costmap_2d::StaticLayer"
map_subscribe_transient_local: True
always_send_full_costmap: 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: False
robot_radius: 0.22
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: /go2/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
raytrace_max_range: 3.0
raytrace_min_range: 0.0
obstacle_max_range: 2.5
obstacle_min_range: 0.0
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: 0.65
always_send_full_costmap: True
map_server:
ros__parameters:
use_sim_time: False
# Overridden in launch by the "map" launch configuration or provided default value.
# To use in yaml, remove the default "map" value in the tb3_simulation_launch.py file & provide full path to map below.
yaml_filename: ""
map_saver:
ros__parameters:
use_sim_time: False
save_map_timeout: 5.0
free_thresh_default: 0.25
occupied_thresh_default: 0.65
map_subscribe_transient_local: True
planner_server:
ros__parameters:
expected_planner_frequency: 20.0
use_sim_time: False
planner_plugins: ["GridBased"]
GridBased:
plugin: "nav2_navfn_planner/NavfnPlanner"
tolerance: 0.5
use_astar: false
allow_unknown: true
smoother_server:
ros__parameters:
use_sim_time: False
smoother_plugins: ["simple_smoother"]
simple_smoother:
plugin: "nav2_smoother::SimpleSmoother"
tolerance: 1.0e-10
max_its: 1000
do_refinement: True
behavior_server:
ros__parameters:
costmap_topic: local_costmap/costmap_raw
footprint_topic: local_costmap/published_footprint
cycle_frequency: 10.0
behavior_plugins: ["spin", "backup", "drive_on_heading", "assisted_teleop", "wait"]
spin:
plugin: "nav2_behaviors/Spin"
backup:
plugin: "nav2_behaviors/BackUp"
drive_on_heading:
plugin: "nav2_behaviors/DriveOnHeading"
wait:
plugin: "nav2_behaviors/Wait"
assisted_teleop:
plugin: "nav2_behaviors/AssistedTeleop"
global_frame: odom
robot_base_frame: base_link
transform_tolerance: 0.1
use_sim_time: False
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: False
waypoint_follower:
ros__parameters:
use_sim_time: False
loop_rate: 20
stop_on_failure: false
waypoint_task_executor_plugin: "wait_at_waypoint"
wait_at_waypoint:
plugin: "nav2_waypoint_follower::WaitAtWaypoint"
enabled: True
waypoint_pause_duration: 200
velocity_smoother:
ros__parameters:
use_sim_time: False
smoothing_frequency: 20.0
scale_velocities: False
feedback: "OPEN_LOOP"
max_velocity: [0.26, 0.0, 1.0]
min_velocity: [-0.26, 0.0, -1.0]
max_accel: [2.5, 0.0, 3.2]
max_decel: [-2.5, 0.0, -3.2]
odom_topic: "/go2/odom"
odom_duration: 0.1
deadband_velocity: [0.0, 0.0, 0.0]
velocity_timeout: 1.0

203
deploy/ros2_nodes/sportmode_nav2/params/nav2_params.yaml
View File

@ -110,74 +110,101 @@ bt_navigator_navigate_to_pose_rclcpp_node:
controller_server:
ros__parameters:
use_sim_time: False
controller_frequency: 20.0
min_x_velocity_threshold: 0.001
min_y_velocity_threshold: 0.5
min_theta_velocity_threshold: 0.001
failure_tolerance: 0.3
progress_checker_plugin: "progress_checker"
goal_checker_plugins: ["general_goal_checker"] # "precise_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
#precise_goal_checker:
# plugin: "nav2_controller::SimpleGoalChecker"
# xy_goal_tolerance: 0.25
# yaw_goal_tolerance: 0.25
# stateful: True
general_goal_checker:
stateful: True
plugin: "nav2_controller::SimpleGoalChecker"
xy_goal_tolerance: 0.5
yaw_goal_tolerance: 1.
# DWB parameters
FollowPath:
plugin: "dwb_core::DWBLocalPlanner"
debug_trajectory_details: True
min_vel_x: 0.0
min_vel_y: 0.0
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
plugin: "nav2_mppi_controller::MPPIController"
time_steps: 56
model_dt: 0.05
batch_size: 2000
vx_std: 0.2
vy_std: 0.2
wz_std: 0.4
vx_max: 0.5
vx_min: -0.35
vy_max: 0.5
wz_max: 1.9
iteration_count: 1
prune_distance: 1.7
transform_tolerance: 0.1
temperature: 0.3
gamma: 0.015
motion_model: "DiffDrive"
visualize: false
TrajectoryVisualizer:
trajectory_step: 5
time_step: 3
AckermannConstraints:
min_turning_r: 0.05
critics: ["ConstraintCritic", "CostCritic", "GoalCritic", "GoalAngleCritic", "PathAlignCritic", "PathFollowCritic", "PathAngleCritic", "PreferForwardCritic"]
ConstraintCritic:
enabled: true
cost_power: 1
cost_weight: 4.0
GoalCritic:
enabled: true
cost_power: 1
cost_weight: 5.0
threshold_to_consider: 1.4
GoalAngleCritic:
enabled: true
cost_power: 1
cost_weight: 3.0
threshold_to_consider: 0.5
PreferForwardCritic:
enabled: true
cost_power: 1
cost_weight: 5.0
threshold_to_consider: 0.5
# ObstaclesCritic:
# enabled: true
# cost_power: 1
# repulsion_weight: 1.5
# critical_weight: 20.0
# consider_footprint: false
# collision_cost: 10000.0
# collision_margin_distance: 0.1
# near_goal_distance: 0.5
CostCritic:
enabled: true
cost_power: 1
cost_weight: 20.0
critical_cost: 300.0
consider_footprint: true
collision_cost: 1000000.0
near_goal_distance: 1.0
PathAlignCritic:
PathAlignCritic:
enabled: true
cost_power: 1
cost_weight: 14.0
max_path_occupancy_ratio: 0.05
trajectory_point_step: 3
threshold_to_consider: 0.5
offset_from_furthest: 20
use_path_orientations: false
PathFollowCritic:
enabled: true
cost_power: 1
cost_weight: 5.0
offset_from_furthest: 5
threshold_to_consider: 1.4
PathAngleCritic:
enabled: true
cost_power: 1
cost_weight: 2.0
offset_from_furthest: 4
threshold_to_consider: 0.5
max_angle_to_furthest: 1.0
forward_preference: true
# VelocityDeadbandCritic:
# enabled: true
# cost_power: 1
# cost_weight: 35.0
# deadband_velocities: [0.05, 0.05, 0.05]
# TwirlingCritic:
# enabled: true
# twirling_cost_power: 1
# twirling_cost_weight: 10.0
local_costmap:
local_costmap:
@ -191,7 +218,8 @@ local_costmap:
width: 3
height: 3
resolution: 0.05
robot_radius: 0.22
# robot_radius: 0.22
footprint: "[ [0.33, 0.2], [0.33 -0.2], [-0.48 -0.2], [-0.48, 0.2] ]"
plugins: ["voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
@ -231,6 +259,7 @@ global_costmap:
robot_base_frame: base_link
use_sim_time: False
robot_radius: 0.22
# footprint: "[ [0.34, 0.2], [0.34 -0.2], [-0.34 -0.2], [-0.34, 0.2] ]"
resolution: 0.05
track_unknown_space: true
plugins: ["static_layer", "obstacle_layer", "inflation_layer"]
@ -254,7 +283,7 @@ global_costmap:
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 3.0
inflation_radius: 0.65
inflation_radius: 0.25
always_send_full_costmap: True
map_server:
@ -272,6 +301,43 @@ map_saver:
occupied_thresh_default: 0.65
map_subscribe_transient_local: True
# planner_server:
# ros__parameters:
# expected_planner_frequency: 1.0
# use_sim_time: False
# planner_plugins: ["GridBased"]
# GridBased:
# plugin: "nav2_smac_planner/SmacPlannerHybrid"
# tolerance: 0.5 # tolerance for planning if unable to reach exact pose, in meters
# downsample_costmap: false # whether or not to downsample the map
# downsampling_factor: 1 # multiplier for the resolution of the costmap layer (e.g. 2 on a 5cm costmap would be 10cm)
# allow_unknown: true # allow traveling in unknown space
# max_iterations: 1000000 # maximum total iterations to search for before failing (in case unreachable), set to -1 to disable
# max_on_approach_iterations: 1000 # maximum number of iterations to attempt to reach goal once in tolerance
# max_planning_time: 3.5 # max time in s for planner to plan, smooth, and upsample. Will scale maximum smoothing and upsampling times based on remaining time after planning.
# motion_model_for_search: "REEDS_SHEPP" # For Hybrid Dubin, Redds-Shepp
# cost_travel_multiplier: 1.0 # For 2D: Cost multiplier to apply to search to steer away from high cost areas. Larger values will place in the center of aisles more exactly (if non-`FREE` cost potential field exists) but take slightly longer to compute. To optimize for speed, a value of 1.0 is reasonable. A reasonable tradeoff value is 2.0. A value of 0.0 effective disables steering away from obstacles and acts like a naive binary search A*.
# angle_quantization_bins: 64 # For Hybrid nodes: Number of angle bins for search, must be 1 for 2D node (no angle search)
# analytic_expansion_ratio: 3.5 # For Hybrid/Lattice nodes: The ratio to attempt analytic expansions during search for final approach.
# analytic_expansion_max_length: 3.0 # For Hybrid/Lattice nodes: The maximum length of the analytic expansion to be considered valid to prevent unsafe shortcutting (in meters). This should be scaled with minimum turning radius and be no less than 4-5x the minimum radius
# minimum_turning_radius: 0.3 # For Hybrid/Lattice nodes: minimum turning radius in m of path / vehicle
# reverse_penalty: 1.1 # For Reeds-Shepp model: penalty to apply if motion is reversing, must be => 1
# change_penalty: 0.2 # For Hybrid nodes: penalty to apply if motion is changing directions, must be >= 0
# non_straight_penalty: 1.20 # For Hybrid nodes: penalty to apply if motion is non-straight, must be => 1
# cost_penalty: 50.0 # For Hybrid nodes: penalty to apply to higher cost areas when adding into the obstacle map dynamic programming distance expansion heuristic. This drives the robot more towards the center of passages. A value between 1.3 - 3.5 is reasonable.
# retrospective_penalty: 0.025 # For Hybrid/Lattice nodes: penalty to prefer later maneuvers before earlier along the path. Saves search time since earlier nodes are not expanded until it is necessary. Must be >= 0.0 and <= 1.0
# rotation_penalty: 2.0 # For Lattice node: Penalty to apply only to pure rotate in place commands when using minimum control sets containing rotate in place primitives. This should always be set sufficiently high to weight against this action unless strictly necessary for obstacle avoidance or there may be frequent discontinuities in the plan where it requests the robot to rotate in place to short-cut an otherwise smooth path for marginal path distance savings.
# lookup_table_size: 20.0 # For Hybrid nodes: Size of the dubin/reeds-sheep distance window to cache, in meters.
# cache_obstacle_heuristic: True # For Hybrid nodes: Cache the obstacle map dynamic programming distance expansion heuristic between subsiquent replannings of the same goal location. Dramatically speeds up replanning performance (40x) if costmap is largely static.
# allow_reverse_expansion: False # For Lattice nodes: Whether to expand state lattice graph in forward primitives or reverse as well, will double the branching factor at each step.
# smooth_path: True # For Lattice/Hybrid nodes: Whether or not to smooth the path, always true for 2D nodes.
# smoother:
# max_iterations: 1000
# w_smooth: 0.3
# w_data: 0.2
# tolerance: 1.0e-10
# do_refinement: true # Whether to recursively run the smoother 3 times on the results from prior runs to refine the results further
planner_server:
ros__parameters:
expected_planner_frequency: 20.0
@ -282,7 +348,6 @@ planner_server:
tolerance: 0.5
use_astar: false
allow_unknown: true
smoother_server:
ros__parameters:
use_sim_time: False

4
deploy/ros2_nodes/sportmode_nav2/params/ros_ekf.yaml
View File

@ -4,7 +4,7 @@ ekf_filter_node:
# The frequency, in Hz, at which the filter will output a position estimate. Note that the filter will not begin
# computation until it receives at least one message from one of the inputs. It will then run continuously at the
# frequency specified here, regardless of whether it receives more measurements. Defaults to 30 if unspecified.
frequency: 50.0
frequency: 30.0
# The period, in seconds, after which we consider a sensor to have timed out. In this event, we carry out a predict
# cycle on the EKF without correcting it. This parameter can be thought of as the minimum frequency with which the
@ -15,7 +15,7 @@ ekf_filter_node:
# set to true, no 3D information will be used in your state estimate. Use this if you are operating in a planar
# environment and want to ignore the effect of small variations in the ground plane that might otherwise be detected
# by, for example, an IMU. Defaults to false if unspecified.
two_d_mode: false
two_d_mode: true
# Use this parameter to provide an offset to the transform generated by ekf_localization_node. This can be used for
# future dating the transform, which is required for interaction with some other packages. Defaults to 0.0 if