使用机器人定位包robot-locallization的robot_localization实现传感器融合ROS 2
在本教程中，我将向您展示如何在模拟移动机器人上设置 robot_localization ROS 2 包。我们将使用robot_localization 包将来自/wheel/odometry主题的里程计数据与来自/imu/data主题的 IMU 数据融合，以提供局部准确、平滑的里程计估计。轮子可能会打滑，因此使用 robot_localization 包可以帮助解决这个问题。
robot_localization包以使用扩展卡尔曼滤波器(ekf_node) 来融合来自传感器输入的数据。这些传感器输入来自我们的 SDF 文件中定义的 IMU Gazebo 插件和差动驱动 Gazebo 插件。
在真实的机器人项目中，我们将使用来自 IMU 传感器（例如BNO055和车轮编码器）的数据，而不是模拟的 IMU 和测距数据。
ekf_node 将订阅以下主题（ROS 消息类型在括号中）：
- /wheel/odometry：基于来自差动驱动 Gazebo 插件的信息的位置和速度估计（在真实的机器人项目中，这将是从车轮编码器滴答计数中提取的信息）。方向是四元数格式。( nav_msgs/Odometry )
- /imu/data：来自惯性测量单元 (IMU) 传感器 Gazebo 插件的数据 ( sensor_msgs/Imu.msg )
该节点将向以下主题发布数据：
- /odometry/filtered : 平滑的里程计信息 ( nav_msgs/Odometry )
- /tf：从odom框架（父）到base_footprint框架（子）的坐标变换。
安装机器人本地化包
让我们从安装robot_localization包开始。打开一个新的终端窗口，然后键入以下命令：
sudo apt install ros-foxy-robot-localization
如果您使用的是较新版本的 ROS 2，例如 ROS 2 Galactic，请键入以下内容：
sudo apt install ros-galactic-robot-localization
上述命令的语法是：
sudo apt install ros-<ros2-distro>-robot-localization
如果您使用的是 ROS 2 Galactic 并且想从源代码构建而不是使用上面的现成包，则需要将 robot_localization 包下载到您的工作区。
cd ~/dev_ws/src
git clone -b fix/galactic/load_parameters https://github.com/nobleo/robot_localization.git
您需要下载上面那个包的原因是，如果您不这样做，导航堆栈将抛出以下异常：
[ekf_node-1] 在抛出“rclcpp::ParameterTypeException”实例后调用终止
[ekf_node-1] what(): expected [string] got [not set]
设置配置参数
我们现在需要通过创建YAML 文件来指定 ekf_node 的配置参数。
打开一个新的终端窗口，然后输入：
colcon_cd basic_mobile_robot
gedit ekf.yaml
将此代码复制并粘贴到 YAML 文件中。
保存并关闭文件。
您可以在此页面上获得所有参数的完整说明。您还可以在 GitHub 上查看此链接以获取示例 ekf.yaml 文件
ekf.yaml
### ekf config file ###
ekf_filter_node:
ros__parameters:
use_sim_time: true
# 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: 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 to broadcast the transformation over the /tf topic. Defaults to true if unspecified.
publish_tf: true
# Whether to publish the acceleration state. Defaults to false if unspecified.
publish_acceleration: false
# If the filter sees a jump back in time, the filter is reset (convenient for testing with rosbags!)
reset_on_time_jump: 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: wheel/odometry
# 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, true,
false, false, false,
true, true, true,
false, false, true,
false, false, false]
# [x_pos , y_pos , z_pos,
# roll , pitch , yaw,
# x_vel , y_vel , z_vel,
# roll_vel, pitch_vel, yaw_vel,
# x_accel , y_accel , z_accel]
# 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
imu0: imu/data
imu0_config: [false, false, false,
true, true, true,
false, false, false,
true, true, true,
true, true, true]
# [x_pos , y_pos , z_pos,
# roll , pitch , yaw,
# x_vel , y_vel , z_vel,
# roll_vel, pitch_vel, yaw_vel,
# x_accel , y_accel , z_accel]
imu0_nodelay: false
imu0_differential: false
imu0_relative: true
imu0_queue_size: 7
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]
创建启动文件
现在转到您的启动文件夹。打开一个新的终端窗口，然后输入：
colcon_cd basic_mobile_robot
cd launch
gedit basic_mobile_bot_v3.launch.py
launch.py
# Author: Addison Sears-Collins
# Date: August 31, 2021
# Description: Launch a basic mobile robot
# https://automaticaddison.com
import os
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription
from launch.conditions import IfCondition, UnlessCondition
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import Command, LaunchConfiguration, PythonExpression
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Set the path to different files and folders.
pkg_gazebo_ros = FindPackageShare(package='gazebo_ros').find('gazebo_ros')
pkg_share = FindPackageShare(package='basic_mobile_robot').find('basic_mobile_robot')
default_launch_dir = os.path.join(pkg_share, 'launch')
default_model_path = os.path.join(pkg_share, 'models/basic_mobile_bot_v1.urdf')
robot_localization_file_path = os.path.join(pkg_share, 'config/ekf.yaml')
robot_name_in_urdf = 'basic_mobile_bot'
default_rviz_config_path = os.path.join(pkg_share, 'rviz/urdf_config.rviz')
world_file_name = 'basic_mobile_bot_world/smalltown.world'
world_path = os.path.join(pkg_share, 'worlds', world_file_name)
# Launch configuration variables specific to simulation
headless = LaunchConfiguration('headless')
model = LaunchConfiguration('model')
rviz_config_file = LaunchConfiguration('rviz_config_file')
use_robot_state_pub = LaunchConfiguration('use_robot_state_pub')
use_rviz = LaunchConfiguration('use_rviz')
use_sim_time = LaunchConfiguration('use_sim_time')
use_simulator = LaunchConfiguration('use_simulator')
world = LaunchConfiguration('world')
# Declare the launch arguments
declare_model_path_cmd = DeclareLaunchArgument(
name='model',
default_value=default_model_path,
description='Absolute path to robot urdf file')
declare_rviz_config_file_cmd = DeclareLaunchArgument(
name='rviz_config_file',
default_value=default_rviz_config_path,
description='Full path to the RVIZ config file to use')
declare_simulator_cmd = DeclareLaunchArgument(
name='headless',
default_value='False',
description='Whether to execute gzclient')
declare_use_robot_state_pub_cmd = DeclareLaunchArgument(
name='use_robot_state_pub',
default_value='True',
description='Whether to start the robot state publisher')
declare_use_rviz_cmd = DeclareLaunchArgument(
name='use_rviz',
default_value='True',
description='Whether to start RVIZ')
declare_use_sim_time_cmd = DeclareLaunchArgument(
name='use_sim_time',
default_value='True',
description='Use simulation (Gazebo) clock if true')
declare_use_simulator_cmd = DeclareLaunchArgument(
name='use_simulator',
default_value='True',
description='Whether to start the simulator')
declare_world_cmd = DeclareLaunchArgument(
name='world',
default_value=world_path,
description='Full path to the world model file to load')
# Specify the actions
# Start Gazebo server
start_gazebo_server_cmd = IncludeLaunchDescription(
PythonLaunchDescriptionSource(os.path.join(pkg_gazebo_ros, 'launch', 'gzserver.launch.py')),
condition=IfCondition(use_simulator),
launch_arguments={'world': world}.items())
# Start Gazebo client
start_gazebo_client_cmd = IncludeLaunchDescription(
PythonLaunchDescriptionSource(os.path.join(pkg_gazebo_ros, 'launch', 'gzclient.launch.py')),
condition=IfCondition(PythonExpression([use_simulator, ' and not ', headless])))
# Start robot localization using an Extended Kalman filter
start_robot_localization_cmd = Node(
package='robot_localization',
executable='ekf_node',
name='ekf_filter_node',
output='screen',
parameters=[robot_localization_file_path,
{'use_sim_time': use_sim_time}])
# Subscribe to the joint states of the robot, and publish the 3D pose of each link.
start_robot_state_publisher_cmd = Node(
condition=IfCondition(use_robot_state_pub),
package='robot_state_publisher',
executable='robot_state_publisher',
parameters=[{'use_sim_time': use_sim_time,
'robot_description': Command(['xacro ', model])}],
arguments=[default_model_path])
# Launch RViz
start_rviz_cmd = Node(
condition=IfCondition(use_rviz),
package='rviz2',
executable='rviz2',
name='rviz2',
output='screen',
arguments=['-d', rviz_config_file])
# Create the launch description and populate
ld = LaunchDescription()
# Declare the launch options
ld.add_action(declare_model_path_cmd)
ld.add_action(declare_rviz_config_file_cmd)
ld.add_action(declare_simulator_cmd)
ld.add_action(declare_use_robot_state_pub_cmd)
ld.add_action(declare_use_rviz_cmd)
ld.add_action(declare_use_sim_time_cmd)
ld.add_action(declare_use_simulator_cmd)
ld.add_action(declare_world_cmd)
# Add any actions
ld.add_action(start_gazebo_server_cmd)
ld.add_action(start_gazebo_client_cmd)
ld.add_action(start_robot_localization_cmd)
ld.add_action(start_robot_state_publisher_cmd)
ld.add_action(start_rviz_cmd)
return ld
https://automaticaddison.com/sensor-fusion-using-the-robot-localization-package-ros-2/#About_the_Robot_Localization_Package
