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Tried to fix ukf

This commit is contained in:
wittenator 2023-11-03 16:13:01 +01:00
parent 60a798f5d2
commit 96c69e859c
8 changed files with 351 additions and 3 deletions
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10
src/dcaiti_control/CMakeLists.txt
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@ -33,9 +33,17 @@ if(BUILD_TESTING)
endif()
install(
DIRECTORY config description launch worlds
DIRECTORY config description launch worlds src
DESTINATION share/${PROJECT_NAME}
)
ament_python_install_package(${PROJECT_NAME})
# Install Python executables
install(PROGRAMS
src/imu_covariance_adder.py
DESTINATION lib/${PROJECT_NAME}
)
ament_environment_hooks("${CMAKE_CURRENT_SOURCE_DIR}/env-hooks/${PROJECT_NAME}.dsv.in")
ament_package()

217
src/dcaiti_control/config/ukf.yml Normal file
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@ -0,0 +1,217 @@
### ukf config file ###
ukf_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: 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
# 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: /ackermann_steering_controller/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: [false, false, false,
false, false, false,
true, 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] 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: lidar/imu_covariance
#imu0_config: [false, false, false,
# true, true, true,
# false, false, false,
# true, true, true,
# true, true, true]
#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: 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.
# Note: the specification of covariance matrices can be cumbersome, so all matrix parameters in this package support
# both full specification or specification of only the diagonal values.
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 diagonal values below
# if unspecified. In this example, we specify only the diagonal of the matrix.
initial_estimate_covariance: [1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9]
# [ADVANCED, UKF ONLY] The alpha and kappa variables control the spread of the sigma points. Unless you are familiar
# with UKFs, it's probably a good idea to leave these alone.
# Defaults to 0.001 if unspecified.
alpha: 0.001
# Defaults to 0 if unspecified.
kappa: 0.0
# [ADVANCED, UKF ONLY] The beta variable relates to the distribution of the state vector. Again, it's probably best to
# leave this alone if you're uncertain. Defaults to 2 if unspecified.
beta: 2.0

0
src/dcaiti_control/dcaiti_control/__init__.py Normal file
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47
src/dcaiti_control/description/imu.xacro
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@ -3,10 +3,57 @@
<gazebo reference="laser_frame">
<material>Gazebo/Red</material>
<sensor name="imu_sensor" type="imu">
<ignition_frame_id>laser_frame</ignition_frame_id>
<always_on>1</always_on>
<update_rate>50</update_rate>
<visualize>false</visualize>
<topic>/lidar/imu</topic>
<angular_velocity>
<x>
<noise>
<type>gaussian</type>
<mean>0.0</mean>
<stddev>0.01</stddev>
</noise>
</x>
<y>
<noise>
<type>gaussian</type>
<mean>0.0</mean>
<stddev>0.01</stddev>
</noise>
</y>
<z>
<noise>
<type>gaussian</type>
<mean>0.0</mean>
<stddev>0.01</stddev>
</noise>
</z>
</angular_velocity>
<linear_acceleration>
<x>
<noise>
<type>gaussian</type>
<mean>0.0</mean>
<stddev>0.01</stddev>
</noise>
</x>
<y>
<noise>
<type>gaussian</type>
<mean>0.0</mean>
<stddev>0.01</stddev>
</noise>
</y>
<z>
<noise>
<type>gaussian</type>
<mean>0.0</mean>
<stddev>0.01</stddev>
</noise>
</z>
</linear_acceleration>
</sensor>
</gazebo>
</robot>

27
src/dcaiti_control/launch/launch_sim.py
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@ -41,7 +41,7 @@ def generate_launch_description():
gazebo = IncludeLaunchDescription(
PythonLaunchDescriptionSource([os.path.join(
get_package_share_directory('ros_gz_sim'), 'launch', 'gz_sim.launch.py')]),
launch_arguments=[('gz_args', [f" -r -v 1 {world_path}/generated_worlds/AU2_skidpad.sdf"])],
launch_arguments=[('gz_args', [f" -r -v 0 {world_path}/generated_worlds/AU2_skidpad.sdf"])],
)
# Run the spawner node from the gazebo_ros package. The entity name doesn't really matter if you only have a single robot.
@ -94,6 +94,29 @@ def generate_launch_description():
on_exit=[diff_drive_spawner],
)
)
imu_covariance_adder = Node(
package='dcaiti_control',
executable='imu_covariance_adder.py',
name='imu_covariance_adder',
output='screen',
parameters=[
{'orientation_covariance': [1e-3]*9},
{'linear_acceleration_covariance': [1e-3]*9},
{'angular_velocity_covariance': [1e-3]*9},
{'subscribe_topic': '/lidar/imu'},
{'publish_topic': '/lidar/imu_covariance'}
]
)
#ukf_node = Node(
# package='robot_localization',
# executable='ukf_node',
# name='ukf_filter_node',
# output='screen',
# parameters=[str(base_path / 'config' / 'ukf.yml')],
# remappings=[('odometry/filtered', 'odometry/local')]
# )
# Launch them all!
@ -112,4 +135,6 @@ def generate_launch_description():
spawn_entity,
delayed_diff_drive_spawner,
delayed_joint_broad_spawner,
imu_covariance_adder,
#ukf_node
])

2
src/dcaiti_control/launch/rsp.launch.py
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@ -67,5 +67,5 @@ def generate_launch_description():
node_robot_state_publisher,
node_joint_state_publisher,
twist_mux,
#twist_mux,
])

0
src/dcaiti_control/src/__init__.py Normal file
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51
src/dcaiti_control/src/imu_covariance_adder.py Executable file
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@ -0,0 +1,51 @@
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Imu
class ImuCovarianceNode(Node):
def __init__(self):
super().__init__('imu_covariance_node')
self.declare_parameter('publish_topic', '/lidar/imu_covariance').value
self.declare_parameter('subscribe_topic', '/lidar/imu').value
self.declare_parameter('linear_acceleration_covariance', [0.0] * 9)
self.declare_parameter('angular_velocity_covariance', [0.0] * 9)
self.declare_parameter('orientation_covariance', [0.0] * 9)
self.orientation_covariance = self.get_parameter('orientation_covariance').value
self.linear_acceleration_covariance = self.get_parameter('linear_acceleration_covariance').value
self.angular_velocity_covariance = self.get_parameter('angular_velocity_covariance').value
self.publish_topic = self.get_parameter('publish_topic').value
self.subscribe_topic = self.get_parameter('subscribe_topic').value
self.publisher = self.create_publisher(Imu, self.publish_topic, 10)
self.subscription = self.create_subscription(
Imu,
self.subscribe_topic,
self.imu_callback,
10)
def imu_callback(self, msg):
# Update covariances in-place
msg.orientation_covariance = self.orientation_covariance
msg.linear_acceleration_covariance = self.linear_acceleration_covariance
msg.angular_velocity_covariance = self.angular_velocity_covariance
self.publisher.publish(msg)
def main(args=None):
rclpy.init(args=args)
imu_covariance_node = ImuCovarianceNode()
rclpy.spin(imu_covariance_node)
imu_covariance_node.destroy_node()
rclpy.shutdown()
if __name__ == '__main__':
main()