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eutobot dependency node skip

master
hojunlim 2023-12-15 16:15:19 +09:00
parent 9d916a95d3
commit c7604fcfa6
7 changed files with 621 additions and 53 deletions
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75
src/.vscode/colcon.env vendored Normal file
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@ -0,0 +1,75 @@
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74
src/eutobot/eutobot_bringup/launch/robot.launch.py
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@ -32,41 +32,23 @@ from launch_ros.parameter_descriptions import ParameterValue
def generate_launch_description():
LDS_MODEL = os.environ['LDS_MODEL']
LDS_LAUNCH_FILE = '/hlds_laser.launch.py'
# Dynamixel USB Port
dynamixel_usb_port = LaunchConfiguration('dynamixel_usb_port', default='/dev/ttyUSB0')
dynamixel_usb_port = LaunchConfiguration('dynamixel_usb_port', default='/dev/ttyUSB1')
eutobot_param_dir = LaunchConfiguration(
'eutobot_param_dir',
default=os.path.join(
get_package_share_directory('eutobot_bringup'),
'param',
'eutobot.yaml'))
# eutobot_param_dir = LaunchConfiguration(
# 'eutobot_param_dir',
# default=os.path.join(
# get_package_share_directory('eutobot_bringup'),
# 'param',
# 'eutobot.yaml'))
if LDS_MODEL == 'LDS-01':
lidar_pkg_dir = LaunchConfiguration(
'lidar_pkg_dir',
default=os.path.join(get_package_share_directory('hls_lfcd_lds_driver'), 'launch'))
elif LDS_MODEL == 'LDS-02':
lidar_pkg_dir = LaunchConfiguration(
'lidar_pkg_dir',
default=os.path.join(get_package_share_directory('ld08_driver'), 'launch'))
LDS_LAUNCH_FILE = '/ld08.launch.py'
elif LDS_MODEL == 'YDLIDAR':
lidar_pkg_dir = LaunchConfiguration(
'lidar_pkg_dir',
default=os.path.join(get_package_share_directory('ydlidar_ros2_driver'), 'launch'))
LDS_LAUNCH_FILE = '/ydlidar_launch.py'
else:
lidar_pkg_dir = LaunchConfiguration(
'lidar_pkg_dir',
default=os.path.join(get_package_share_directory('hls_lfcd_lds_driver'), 'launch'))
lidar_pkg_dir = LaunchConfiguration(
'lidar_pkg_dir',
default=os.path.join(get_package_share_directory('ydlidar_ros2_driver'), 'launch'))
# LDS_LAUNCH_FILE = '/ydlidar_launch.py'
use_sim_time = LaunchConfiguration('use_sim_time', default='false')
# YAHBOOM
yahboom_pkg_dir = LaunchConfiguration(
'yahboom_pkg_dir',
@ -79,19 +61,19 @@ def generate_launch_description():
default_value=use_sim_time,
description='Use simulation (Gazebo) clock if true'),
DeclareLaunchArgument(
'eutobot_param_dir',
default_value=eutobot_param_dir,
description='Full path to eutobot parameter file to load'),
# DeclareLaunchArgument(
# 'eutobot_param_dir',
# default_value=eutobot_param_dir,
# description='Full path to eutobot parameter file to load'),
# IncludeLaunchDescription(
# PythonLaunchDescriptionSource(
# [ThisLaunchFileDir(), '/eutobot_state_publisher.launch.py']),
# launch_arguments={'use_sim_time': use_sim_time}.items(),
# ),
IncludeLaunchDescription(
PythonLaunchDescriptionSource(
[ThisLaunchFileDir(), '/eutobot_state_publisher.launch.py']),
launch_arguments={'use_sim_time': use_sim_time}.items(),
),
IncludeLaunchDescription(
PythonLaunchDescriptionSource([lidar_pkg_dir, LDS_LAUNCH_FILE]),
PythonLaunchDescriptionSource([lidar_pkg_dir, '/ydlidar_launch.py']),
launch_arguments={'port': '/dev/ttySAC6', 'frame_id': 'base_scan'}.items(),
),
@ -99,10 +81,10 @@ def generate_launch_description():
PythonLaunchDescriptionSource([yahboom_pkg_dir, "/yahboomcar_bringup_X3_launch.py"]),
),
Node(
package='eutobot_node',
executable='eutobot_ros',
parameters=[eutobot_param_dir],
arguments=['-i', dynamixel_usb_port],
output='screen'),
# Node(
# package='eutobot_node',
# executable='eutobot_ros',
# parameters=[eutobot_param_dir],
# arguments=['-i', dynamixel_usb_port],
# output='screen'),
])

16
src/yahboomcar_bringup/launch/yahboomcar_bringup_X3_launch.py
View File

@ -65,6 +65,12 @@ def generate_launch_description():
'imu_filter_param.yaml'
)
ekf_filter_config = os.path.join(
get_package_share_directory('yahboomcar_bringup'),
'param',
'ekf.yaml'
)
driver_node = Node(
package='yahboomcar_bringup',
executable='Mcnamu_driver_X3',
@ -88,13 +94,11 @@ def generate_launch_description():
executable='ekf_node',
name='ekf_filter_node',
output='screen',
parameters=[
os.path.join(get_package_share_directory("robot_localization"), 'params', 'ekf.yaml')
],
parameters=[ekf_filter_config],
remappings=[
("/example/imu", "/imu/data"),
("/example/odom", "/odom_raw"),
("/odometry/filtered", "/odom"),
('/odom0', '/odom_raw'),
('/imu0', '/imu/data'),
('/odometry/filtered', '/odom')
]
)

254
src/yahboomcar_bringup/param/ekf.yaml Normal file
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@ -0,0 +1,254 @@
### ekf config file ###
ekf_filter_node:
ros__parameters:
# 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: 20.0
# 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
# filter will generate new output. Defaults to 1 / frequency if not specified.
sensor_timeout: 0.1
# ekf_localization_node and ukf_localization_node both use a 3D omnidirectional motion model. If this parameter is
# 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: 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
# unspecified.
transform_time_offset: 0.0
# Use this parameter to provide specify how long the tf listener should wait for a transform to become available.
# Defaults to 0.0 if unspecified.
transform_timeout: 0.0
# If you're having trouble, try setting this to true, and then echo the /diagnostics_agg topic to see if the node is
# unhappy with any settings or data.
print_diagnostics: true
# Debug settings. Not for the faint of heart. Outputs a ludicrous amount of information to the file specified by
# debug_out_file. I hope you like matrices! Please note that setting this to true will have strongly deleterious
# effects on the performance of the node. Defaults to false if unspecified.
debug: false
# Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path.
debug_out_file: /path/to/debug/file.txt
# Whether we'll allow old measurements to cause a re-publication of the updated state
permit_corrected_publication: false
# Whether to publish the acceleration state. Defaults to false if unspecified.
publish_acceleration: false
# Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified.
#publish_tf: true
# REP-105 (http://www.ros.org/reps/rep-0105.html) specifies four principal coordinate frames: base_link, odom, map, and
# earth. base_link is the coordinate frame that is affixed to the robot. Both odom and map are world-fixed frames.
# The robot's position in the odom frame will drift over time, but is accurate in the short term and should be
# continuous. The odom frame is therefore the best frame for executing local motion plans. The map frame, like the odom
# frame, is a world-fixed coordinate frame, and while it contains the most globally accurate position estimate for your
# robot, it is subject to discrete jumps, e.g., due to the fusion of GPS data or a correction from a map-based
# localization node. The earth frame is used to relate multiple map frames by giving them a common reference frame.
# ekf_localization_node and ukf_localization_node are not concerned with the earth frame.
# Here is how to use the following settings:
# 1. Set the map_frame, odom_frame, and base_link frames to the appropriate frame names for your system.
# 1a. If your system does not have a map_frame, just remove it, and make sure "world_frame" is set to the value of
# odom_frame.
# 2. If you are fusing continuous position data such as wheel encoder odometry, visual odometry, or IMU data, set
# "world_frame" to your odom_frame value. This is the default behavior for robot_localization's state estimation nodes.
# 3. If you are fusing global absolute position data that is subject to discrete jumps (e.g., GPS or position updates
# from landmark observations) then:
# 3a. Set your "world_frame" to your map_frame value
# 3b. MAKE SURE something else is generating the odom->base_link transform. Note that this can even be another state
# estimation node from robot_localization! However, that instance should *not* fuse the global data.
map_frame: map # Defaults to "map" if unspecified
odom_frame: odom # Defaults to "odom" if unspecified
base_link_frame: base_footprint # Defaults to "base_link" if unspecified
world_frame: odom # Defaults to the value of odom_frame if unspecified
# The filter accepts an arbitrary number of inputs from each input message type (nav_msgs/Odometry,
# geometry_msgs/PoseWithCovarianceStamped, geometry_msgs/TwistWithCovarianceStamped,
# sensor_msgs/Imu). To add an input, simply append the next number in the sequence to its "base" name, e.g., odom0,
# odom1, twist0, twist1, imu0, imu1, imu2, etc. The value should be the topic name. These parameters obviously have no
# default values, and must be specified.
odom0: /odom0
# Each sensor reading updates some or all of the filter's state. These options give you greater control over which
# values from each measurement are fed to the filter. For example, if you have an odometry message as input, but only
# want to use its Z position value, then set the entire vector to false, except for the third entry. The order of the
# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Note that not some message types
# do not provide some of the state variables estimated by the filter. For example, a TwistWithCovarianceStamped message
# has no pose information, so the first six values would be meaningless in that case. Each vector defaults to all false
# if unspecified, effectively making this parameter required for each sensor.
odom0_config: [false, false, false,
false, false, false,
true, true, false,
false, false, true,
false, false, false]
# If you have high-frequency data or are running with a low frequency parameter value, then you may want to increase
# the size of the subscription queue so that more measurements are fused.
odom0_queue_size: 1
# [ADVANCED] Large messages in ROS can exhibit strange behavior when they arrive at a high frequency. This is a result
# of Nagle's algorithm. This option tells the ROS subscriber to use the tcpNoDelay option, which disables Nagle's
# algorithm.
odom0_nodelay: false
# [ADVANCED] When measuring one pose variable with two sensors, a situation can arise in which both sensors under-
# report their covariances. This can lead to the filter rapidly jumping back and forth between each measurement as they
# arrive. In these cases, it often makes sense to (a) correct the measurement covariances, or (b) if velocity is also
# measured by one of the sensors, let one sensor measure pose, and the other velocity. However, doing (a) or (b) isn't
# always feasible, and so we expose the differential parameter. When differential mode is enabled, all absolute pose
# data is converted to velocity data by differentiating the absolute pose measurements. These velocities are then
# integrated as usual. NOTE: this only applies to sensors that provide pose measurements; setting differential to true
# for twist measurements has no effect.
odom0_differential: true
# [ADVANCED] When the node starts, if this parameter is true, then the first measurement is treated as a "zero point"
# for all future measurements. While you can achieve the same effect with the differential paremeter, the key
# difference is that the relative parameter doesn't cause the measurement to be converted to a velocity before
# integrating it. If you simply want your measurements to start at 0 for a given sensor, set this to true.
odom0_relative: false
# [ADVANCED] If your data is subject to outliers, use these threshold settings, expressed as Mahalanobis distances, to
# control how far away from the current vehicle state a sensor measurement is permitted to be. Each defaults to
# numeric_limits<double>::max() if unspecified. It is strongly recommended that these parameters be removed if not
# required. Data is specified at the level of pose and twist variables, rather than for each variable in isolation.
# For messages that have both pose and twist data, the parameter specifies to which part of the message we are applying
# the thresholds.
odom0_pose_rejection_threshold: 5.0
odom0_twist_rejection_threshold: 1.0
# Further input parameter examples
odom1: example/odom2
odom1_config: [false, false, true,
false, false, false,
false, false, false,
false, false, true,
false, false, false]
odom1_differential: false
odom1_relative: true
odom1_queue_size: 2
odom1_pose_rejection_threshold: 2.0
odom1_twist_rejection_threshold: 0.2
odom1_nodelay: false
pose0: example/pose
pose0_config: [true, true, false,
false, false, false,
false, false, false,
false, false, false,
false, false, false]
pose0_differential: true
pose0_relative: false
pose0_queue_size: 5
pose0_rejection_threshold: 2.0 # Note the difference in parameter name
pose0_nodelay: false
twist0: example/twist
twist0_config: [false, false, false,
false, false, false,
true, true, true,
false, false, false,
false, false, false]
twist0_queue_size: 3
twist0_rejection_threshold: 2.0
twist0_nodelay: false
imu0: /imu0
imu0_config: [false, false, false,
true, true, true,
false, false, false,
true, true, true,
false, false, false]
#imu0_nodelay: false
imu0_differential: true
imu0_relative: false
imu0_queue_size: 1
imu0_pose_rejection_threshold: 0.8 # Note the difference in parameter names
imu0_twist_rejection_threshold: 0.8 #
imu0_linear_acceleration_rejection_threshold: 0.8 #
# [ADVANCED] Some IMUs automatically remove acceleration due to gravity, and others don't. If yours doesn't, please set
# this to true, and *make sure* your data conforms to REP-103, specifically, that the data is in ENU frame.
imu0_remove_gravitational_acceleration: true
# [ADVANCED] The EKF and UKF models follow a standard predict/correct cycle. During prediction, if there is no
# acceleration reference, the velocity at time t+1 is simply predicted to be the same as the velocity at time t. During
# correction, this predicted value is fused with the measured value to produce the new velocity estimate. This can be
# problematic, as the final velocity will effectively be a weighted average of the old velocity and the new one. When
# this velocity is the integrated into a new pose, the result can be sluggish covergence. This effect is especially
# noticeable with LIDAR data during rotations. To get around it, users can try inflating the process_noise_covariance
# for the velocity variable in question, or decrease the variance of the variable in question in the measurement
# itself. In addition, users can also take advantage of the control command being issued to the robot at the time we
# make the prediction. If control is used, it will get converted into an acceleration term, which will be used during
# predicition. Note that if an acceleration measurement for the variable in question is available from one of the
# inputs, the control term will be ignored.
# Whether or not we use the control input during predicition. Defaults to false.
use_control: false
# Whether the input (assumed to be cmd_vel) is a geometry_msgs/Twist or geometry_msgs/TwistStamped message. Defaults to
# false.
stamped_control: false
# The last issued control command will be used in prediction for this period. Defaults to 0.2.
control_timeout: 0.2
# Which velocities are being controlled. Order is vx, vy, vz, vroll, vpitch, vyaw.
control_config: [true, false, false, false, false, true]
# Places limits on how large the acceleration term will be. Should match your robot's kinematics.
acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4]
# Acceleration and deceleration limits are not always the same for robots.
deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5]
# If your robot cannot instantaneously reach its acceleration limit, the permitted change can be controlled with these
# gains
acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9]
# If your robot cannot instantaneously reach its deceleration limit, the permitted change can be controlled with these
# gains
deceleration_gains: [1.0, 0.0, 0.0, 0.0, 0.0, 1.0]
# [ADVANCED] The process noise covariance matrix can be difficult to tune, and can vary for each application, so it is
# exposed as a configuration parameter. This matrix represents the noise we add to the total error after each
# prediction step. The better the omnidirectional motion model matches your system, the smaller these values can be.
# However, if users find that a given variable is slow to converge, one approach is to increase the
# process_noise_covariance diagonal value for the variable in question, which will cause the filter's predicted error
# to be larger, which will cause the filter to trust the incoming measurement more during correction. The values are
# ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below if
# unspecified.
process_noise_covariance: [0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.015]
# [ADVANCED] This represents the initial value for the state estimate error covariance matrix. Setting a diagonal
# value (variance) to a large value will result in rapid convergence for initial measurements of the variable in
# question. Users should take care not to use large values for variables that will not be measured directly. The values
# are ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below
#if unspecified.
initial_estimate_covariance: [1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9]

253
src/yahboomcar_bringup/param/ekf_origin.yaml Normal file
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@ -0,0 +1,253 @@
### ekf config file ###
ekf_filter_node:
ros__parameters:
# 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: 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
# filter will generate new output. Defaults to 1 / frequency if not specified.
sensor_timeout: 0.1
# ekf_localization_node and ukf_localization_node both use a 3D omnidirectional motion model. If this parameter is
# 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
# 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
# unspecified.
transform_time_offset: 0.0
# Use this parameter to provide specify how long the tf listener should wait for a transform to become available.
# Defaults to 0.0 if unspecified.
transform_timeout: 0.0
# If you're having trouble, try setting this to true, and then echo the /diagnostics_agg topic to see if the node is
# unhappy with any settings or data.
print_diagnostics: true
# Debug settings. Not for the faint of heart. Outputs a ludicrous amount of information to the file specified by
# debug_out_file. I hope you like matrices! Please note that setting this to true will have strongly deleterious
# effects on the performance of the node. Defaults to false if unspecified.
debug: false
# Defaults to "robot_localization_debug.txt" if unspecified. Please specify the full path.
debug_out_file: /path/to/debug/file.txt
# Whether we'll allow old measurements to cause a re-publication of the updated state
permit_corrected_publication: false
# Whether to publish the acceleration state. Defaults to false if unspecified.
publish_acceleration: false
# Whether to broadcast the transformation over the /tf topic. Defaults to true if unspecified.
publish_tf: true
# REP-105 (http://www.ros.org/reps/rep-0105.html) specifies four principal coordinate frames: base_link, odom, map, and
# earth. base_link is the coordinate frame that is affixed to the robot. Both odom and map are world-fixed frames.
# The robot's position in the odom frame will drift over time, but is accurate in the short term and should be
# continuous. The odom frame is therefore the best frame for executing local motion plans. The map frame, like the odom
# frame, is a world-fixed coordinate frame, and while it contains the most globally accurate position estimate for your
# robot, it is subject to discrete jumps, e.g., due to the fusion of GPS data or a correction from a map-based
# localization node. The earth frame is used to relate multiple map frames by giving them a common reference frame.
# ekf_localization_node and ukf_localization_node are not concerned with the earth frame.
# Here is how to use the following settings:
# 1. Set the map_frame, odom_frame, and base_link frames to the appropriate frame names for your system.
# 1a. If your system does not have a map_frame, just remove it, and make sure "world_frame" is set to the value of
# odom_frame.
# 2. If you are fusing continuous position data such as wheel encoder odometry, visual odometry, or IMU data, set
# "world_frame" to your odom_frame value. This is the default behavior for robot_localization's state estimation nodes.
# 3. If you are fusing global absolute position data that is subject to discrete jumps (e.g., GPS or position updates
# from landmark observations) then:
# 3a. Set your "world_frame" to your map_frame value
# 3b. MAKE SURE something else is generating the odom->base_link transform. Note that this can even be another state
# estimation node from robot_localization! However, that instance should *not* fuse the global data.
map_frame: map # Defaults to "map" if unspecified
odom_frame: odom # Defaults to "odom" if unspecified
base_link_frame: base_link # Defaults to "base_link" if unspecified
world_frame: odom # Defaults to the value of odom_frame if unspecified
# The filter accepts an arbitrary number of inputs from each input message type (nav_msgs/Odometry,
# geometry_msgs/PoseWithCovarianceStamped, geometry_msgs/TwistWithCovarianceStamped,
# sensor_msgs/Imu). To add an input, simply append the next number in the sequence to its "base" name, e.g., odom0,
# odom1, twist0, twist1, imu0, imu1, imu2, etc. The value should be the topic name. These parameters obviously have no
# default values, and must be specified.
odom0: example/odom
# Each sensor reading updates some or all of the filter's state. These options give you greater control over which
# values from each measurement are fed to the filter. For example, if you have an odometry message as input, but only
# want to use its Z position value, then set the entire vector to false, except for the third entry. The order of the
# values is x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Note that not some message types
# do not provide some of the state variables estimated by the filter. For example, a TwistWithCovarianceStamped message
# has no pose information, so the first six values would be meaningless in that case. Each vector defaults to all false
# if unspecified, effectively making this parameter required for each sensor.
odom0_config: [true, true, false,
false, false, false,
false, false, false,
false, false, true,
false, false, false]
# If you have high-frequency data or are running with a low frequency parameter value, then you may want to increase
# the size of the subscription queue so that more measurements are fused.
odom0_queue_size: 2
# [ADVANCED] Large messages in ROS can exhibit strange behavior when they arrive at a high frequency. This is a result
# of Nagle's algorithm. This option tells the ROS subscriber to use the tcpNoDelay option, which disables Nagle's
# algorithm.
odom0_nodelay: false
# [ADVANCED] When measuring one pose variable with two sensors, a situation can arise in which both sensors under-
# report their covariances. This can lead to the filter rapidly jumping back and forth between each measurement as they
# arrive. In these cases, it often makes sense to (a) correct the measurement covariances, or (b) if velocity is also
# measured by one of the sensors, let one sensor measure pose, and the other velocity. However, doing (a) or (b) isn't
# always feasible, and so we expose the differential parameter. When differential mode is enabled, all absolute pose
# data is converted to velocity data by differentiating the absolute pose measurements. These velocities are then
# integrated as usual. NOTE: this only applies to sensors that provide pose measurements; setting differential to true
# for twist measurements has no effect.
odom0_differential: false
# [ADVANCED] When the node starts, if this parameter is true, then the first measurement is treated as a "zero point"
# for all future measurements. While you can achieve the same effect with the differential paremeter, the key
# difference is that the relative parameter doesn't cause the measurement to be converted to a velocity before
# integrating it. If you simply want your measurements to start at 0 for a given sensor, set this to true.
odom0_relative: false
# [ADVANCED] If your data is subject to outliers, use these threshold settings, expressed as Mahalanobis distances, to
# control how far away from the current vehicle state a sensor measurement is permitted to be. Each defaults to
# numeric_limits<double>::max() if unspecified. It is strongly recommended that these parameters be removed if not
# required. Data is specified at the level of pose and twist variables, rather than for each variable in isolation.
# For messages that have both pose and twist data, the parameter specifies to which part of the message we are applying
# the thresholds.
odom0_pose_rejection_threshold: 5.0
odom0_twist_rejection_threshold: 1.0
# Further input parameter examples
odom1: example/odom2
odom1_config: [false, false, true,
false, false, false,
false, false, false,
false, false, true,
false, false, false]
odom1_differential: false
odom1_relative: true
odom1_queue_size: 2
odom1_pose_rejection_threshold: 2.0
odom1_twist_rejection_threshold: 0.2
odom1_nodelay: false
pose0: example/pose
pose0_config: [true, true, false,
false, false, false,
false, false, false,
false, false, false,
false, false, false]
pose0_differential: true
pose0_relative: false
pose0_queue_size: 5
pose0_rejection_threshold: 2.0 # Note the difference in parameter name
pose0_nodelay: false
twist0: example/twist
twist0_config: [false, false, false,
false, false, false,
true, true, true,
false, false, false,
false, false, false]
twist0_queue_size: 3
twist0_rejection_threshold: 2.0
twist0_nodelay: false
imu0: example/imu
imu0_config: [false, false, false,
true, true, true,
false, false, false,
true, true, true,
true, true, true]
imu0_nodelay: false
imu0_differential: false
imu0_relative: true
imu0_queue_size: 5
imu0_pose_rejection_threshold: 0.8 # Note the difference in parameter names
imu0_twist_rejection_threshold: 0.8 #
imu0_linear_acceleration_rejection_threshold: 0.8 #
# [ADVANCED] Some IMUs automatically remove acceleration due to gravity, and others don't. If yours doesn't, please set
# this to true, and *make sure* your data conforms to REP-103, specifically, that the data is in ENU frame.
imu0_remove_gravitational_acceleration: true
# [ADVANCED] The EKF and UKF models follow a standard predict/correct cycle. During prediction, if there is no
# acceleration reference, the velocity at time t+1 is simply predicted to be the same as the velocity at time t. During
# correction, this predicted value is fused with the measured value to produce the new velocity estimate. This can be
# problematic, as the final velocity will effectively be a weighted average of the old velocity and the new one. When
# this velocity is the integrated into a new pose, the result can be sluggish covergence. This effect is especially
# noticeable with LIDAR data during rotations. To get around it, users can try inflating the process_noise_covariance
# for the velocity variable in question, or decrease the variance of the variable in question in the measurement
# itself. In addition, users can also take advantage of the control command being issued to the robot at the time we
# make the prediction. If control is used, it will get converted into an acceleration term, which will be used during
# predicition. Note that if an acceleration measurement for the variable in question is available from one of the
# inputs, the control term will be ignored.
# Whether or not we use the control input during predicition. Defaults to false.
use_control: true
# Whether the input (assumed to be cmd_vel) is a geometry_msgs/Twist or geometry_msgs/TwistStamped message. Defaults to
# false.
stamped_control: false
# The last issued control command will be used in prediction for this period. Defaults to 0.2.
control_timeout: 0.2
# Which velocities are being controlled. Order is vx, vy, vz, vroll, vpitch, vyaw.
control_config: [true, false, false, false, false, true]
# Places limits on how large the acceleration term will be. Should match your robot's kinematics.
acceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 3.4]
# Acceleration and deceleration limits are not always the same for robots.
deceleration_limits: [1.3, 0.0, 0.0, 0.0, 0.0, 4.5]
# If your robot cannot instantaneously reach its acceleration limit, the permitted change can be controlled with these
# gains
acceleration_gains: [0.8, 0.0, 0.0, 0.0, 0.0, 0.9]
# If your robot cannot instantaneously reach its deceleration limit, the permitted change can be controlled with these
# gains
deceleration_gains: [1.0, 0.0, 0.0, 0.0, 0.0, 1.0]
# [ADVANCED] The process noise covariance matrix can be difficult to tune, and can vary for each application, so it is
# exposed as a configuration parameter. This matrix represents the noise we add to the total error after each
# prediction step. The better the omnidirectional motion model matches your system, the smaller these values can be.
# However, if users find that a given variable is slow to converge, one approach is to increase the
# process_noise_covariance diagonal value for the variable in question, which will cause the filter's predicted error
# to be larger, which will cause the filter to trust the incoming measurement more during correction. The values are
# ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below if
# unspecified.
process_noise_covariance: [0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.025, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.015]
# [ADVANCED] This represents the initial value for the state estimate error covariance matrix. Setting a diagonal
# value (variance) to a large value will result in rapid convergence for initial measurements of the variable in
# question. Users should take care not to use large values for variables that will not be measured directly. The values
# are ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below
#if unspecified.
initial_estimate_covariance: [1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1e-9]

2
src/yahboomcar_bringup/yahboomcar_bringup/Mcnamu_driver_X3.py
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@ -30,7 +30,7 @@ class yahboomcar_driver(Node):
global car_type_dic
self.RA2DE = 180 / pi
# self.car = Rosmaster()
self.car = Rosmaster(1, "/dev/ttyUSB1", 2, False);
self.car = Rosmaster(1, "/dev/ttyUSB0", 2, False);
self.car.set_car_type(1)
#get parameter
self.declare_parameter('car_type', 'X3')

BIN
src/yahboomcar_bringup/yahboomcar_bringup/__pycache__/Mcnamu_driver_X3.cpython-310.pyc
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