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cra-ros-pkg / robot_localization

robot_localization is a package of nonlinear state estimation nodes. The package was developed by Charles River Analytics, Inc. Please ask questions on answers.ros.org.
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How does robot_localization implement tf conversion from / odom to base_link? #650

Closed miku54 closed 3 years ago

miku54 commented 3 years ago

Bug report

Required Info:

Expected behavior

1、star tf transformation from running "base_footprint" to the sensor 2、star the ekf_localization_node

Actual behavior

I found no odom or "odom_combined" in tf_tree, and I didn't set the conversion from "odom_combined" to "base_footprint" anywhere else.

Additional information

tf_tree should be ：odom_combined>base_footprint>base_link>imu_link

SteveMacenski commented 3 years ago

Please read the documentation -- this is answered in the large body of documentation available

miku54 commented 3 years ago

my ekf config file is: `### 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: 2.0

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: false

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

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_combined           # 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: 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: [false, false, false,
                   false, false, true,
                   true,  true,  false,
                   false, false, false,
                   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: 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::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: mobile_base/sensors/imu_data
    imu0_config: [false, false, false,
                  false, false, false,
                  false, false, false,
                  false, false, true,
                  true,  false, false]

    imu0_nodelay: false
    imu0_differential: true
    imu0_relative: false
    imu0_queue_size: 4
    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]

`

miku54 commented 3 years ago

I read most of forums posts on ROS.org http://ros.org/ but cannot find explicit answer to my concerns

Steve Macenski @.***> 于2021年4月14日周三 上午10:16写道：

Closed #650 https://github.com/cra-ros-pkg/robot_localization/issues/650 .

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