Skip to content

Latest commit

 

History

History
569 lines (485 loc) · 25.2 KB

README.md

File metadata and controls

569 lines (485 loc) · 25.2 KB
Raw

Robot Calibration

This package offers several ROS2 nodes. The primary one is called calibrate, and can be used to calibrate a number of parameters of a robot, such as:

  • 3D Camera intrinsics and extrinsics
  • Joint angle offsets
  • Robot frame offsets

These parameters are then inserted into an updated URDF, or updated camera configuration YAML in the case of camera intrinsics.

Two additional ROS nodes are used for mobile-base related parameter tuning:

  • base_calibration_node - can determine scaling factors for wheel diameter, track width and gyro gain by moving and rotating the robot while tracking the actual movement based on the laser scanner view of a wall.
  • magnetometer_calibration - can be used to do hard iron calibration of a magnetometer.

The calibrate node

Calibration works in two steps. The first step involves the capture of data samples from the robot. Each "sample" comprises the measured joint positions of the robot and two or more "observations". An observation is a collection of points that have been detected by a "sensor". For instance, a robot could use a camera and an arm to "detect" the pose of corners on a checkerboard. In the case of the camera sensor, the collection of points is simply the detected positions of each corner of the checkerboard, relative to the pose of the camera reference frame. For the arm, it is assumed that the checkerboard is fixed relative to a virtual checkerboard frame which is fixed relative to the end effector of the arm. Within the virtual frame, we know the ideal position of each point of the checkerboard corners since the checkerboard is of known size.

The second step of calibration involves optimization of the robot parameters to minimize the errors. Errors are defined as the difference in the pose of the points based on reprojection throuhg each sensor. In the case of our checkerboard above, the transform between the virtual frame and the end effector becomes additional free parameters. By estimating these parameters alongside the robot parameters, we can find a set of parameters such that the reprojection of the checkerboard corners through the arm is as closely aligned with the reprojection through the camera (and any associated kinematic chain, for instance, a pan/tilt head).

Configuration is typically handled through two sets of YAML files: usually called capture.yaml and calibrate.yaml.

If you want to manually move the robot to poses and capture each time you hit ENTER on the keyboard, you can run robot calibration with:

ros2 run robot_calibration calibrate --manual --ros-args --params-file path-to-capture.yaml --params-file path-to-calibrate.yaml

More commonly, you will generate a third YAML file with the capture pose configuration (as documented below in the section "Calibration Poses"):

ros2 run robot_calibration calibrate path-to-calibration-poses.yaml --ros-args --params-file path-to-capture.yaml --params-file path-to-calibrate.yaml

This is often wrapped into a ROS 2 launch file, which often records a bagfile of the observations allowing to re-run just the calibration part instead of needing to run capture each time. For an example, see the UBR-1 example in the next section.

Example Configuration

All of the parameters that can be defined in the capture and calibrate steps are documented below, but sometimes it is just nice to have a full example. The UBR-1 robot uses this package to calibrate in ROS2. Start with the calibrate_launch.py in ubr1_calibration.

Capture Configuration

The capture.yaml file specifies the details needed for data capture:

  • chains - A parameter listing the names of the kinematic chains of the robot which should be controlled.
  • features - A parameter listing the names of the various "feature finders" that will be making our observations at each sample pose.

Each of these chains and features is then defined by a parameter block of the same name, for example:

robot_calibration:
  ros_parameters:
    # List of chains
    chains:
    - arm
    # List of features
    features:
    - checkerboard_finder
    # Parameter block to define the arm chain
    arm:
      topic: /arm_controller/follow_joint_trajectory
      joints:
      - first_joint
      - second_joint
    # Parameter block to define the feature finder:
    checkerboard_finder:
      type: robot_calibration::CheckerboardFinder
      topic: /head_camera/depth_registered/points
      camera_sensor_name: camera
      chain_sensor_name: arm

Chain Parameters

For each chain, the following parameters can be defined:

  • topic - The namespace of the control_msgs::FollowJointTrajectory server used to control this chain.
  • planning_group - Optional parameter, when set to a non-empty string robot_calibration will call MoveIt to plan a collision free path from the current robot pose to the next capture pose. When this parameter is not set, the trajectory simply interpolates from the current pose to the next capture pose without collision awareness - so you need to be careful when defining your series of capture poses.
  • joints - A list of joints that this group comprises.

Finder Parameters

At a minimum, the following parameters must be set for all finders:

  • type - Name of the plugin to load.
  • camera_sensor_name - Every finder outputs observations from some sensor - this name must match the name used later in calibrate.yaml.
  • chain_sensor_name - Every finder outputs observations from some chain - this name must match the name used later in calibrate.yaml.
  • debug - Most finders have a debug parameter which will insert the raw image or point cloud into the observation. This makes the capture bagfile larger but aids in debugging.

The following types are currently included with robot_calibration although you can create your own plugins. Each finder has it's own additional parameters:

  • robot_calibration::CheckerboardFinder - Detects checkerboards in a point cloud.
    • topic - Name of topic of type sensor_msgs::PointCloud2.
    • points_x - Number of corners in the X direction of the checkerboard.
    • points_y - Number of corners in the Y direction of the checkerboard.
    • size - Size of checkerboard squares, in meters.
  • robot_calibration::CheckerboardFinder2d - Detects checkerboards in an image:
    • topic - Name of topic of type sensor_msgs::Image.
    • points_x - Number of corners in the X direction of the checkerboard.
    • points_y - Number of corners in the Y direction of the checkerboard.
    • size - Size of checkerboard squares, in meters.
  • robot_calibration::LedFinder - controls and detects a series of LEDs, which can be a built-in alternative to having a robot hold the checkerboard.
    • gripper_led_action - Namespace of the gripper LED action server.
    • topic - Name of topic of type sensor_msgs::PointCloud2.
    • max_error - Maximum distance detected LED can be from expected pose, in meters.
    • max_inconsistency - Maximum relative difference between two LEDs in the same capture pose, in meters.
    • max_iterations - Maximum number of times to toggle the LEDs for a given capture pose.
    • gripper_led_frame - The robot link which the leds are defined in.
    • leds - Definition of the LED poses. For each LED, you need to specify a code which is sent to the action server to turn that LED on, as well as x, y, and z offsets relative to gripper_led_frame for the expected pose of that LED. These values will be used to generate the chain observation.
  • robot_calibration::PlaneFinder - Detects planes in a point cloud. This will filter out points outside the limits, and then iteratively find the largest plane until a desired one is found. This is commonly used to align a sensor with the ground.
    • topic - Name of topic of type sensor_msgs::PointCloud2.
    • points_max - Maximum number of points to use in the observation
    • The cloud can be pre-filtered using the min_x, max_x, min_y, max_y, min_z, and max_z parameters.
    • The desired orientation of the plane can be used by setting normal_a, normal_b, normal_c parameters - if all are 0, the biggest plane will be selected regardless of orientation. This is particularly useful if the robot might be looking partially at the wall as well as the desired floor surface.
    • normal_angle - If a desired orientation vector is set, the candidate plane normal must be within this angle of the desired normal, in radians.
  • robot_calibration::ScanFinder - Detects points in a laser scan, and then repeats them vertically. This can be used to align a laser scanner against a plane detected by a 3d camera.
    • topic - Name of topic of type sensor_msgs::LaserScan.
    • transform_frame -- Frame to transform the laser scan into, usually base_link.
    • min_x, max_x, min_y, and max_y are used to limit the laser scan points that are used. They are defined in the transform_frame.
    • z_repeats - How many times to copy the points vertically.
    • z_offset - Distance between repeated points.

Additionally, any finder that subscribes to a depth camera has the following parameters:

  • camera_info_topic: The topic name for the camera info.
  • camera_driver: Namespace of the camera driver, only used for Primesense-like devices which have z_offset_mm and z_scaling parameters.

Calibration Configuration

The calibrate.yaml configuration file specifies the configuration for optimization. This specifies several items:

  • base_link - Frame used for internal calculations. Typically, the root of the URDF is used. Often base_link.
  • calibration_steps - In ROS 2, multistep calibration is fully supported. The parameter calibration_steps should be a list of step names. A majority of calibrations probably only use a single step, but the step name must still be in a YAML list format.
robot_calibration:
  ros__parameters:
    base_link: torso_lift_link
    calibration_steps:
    - single_calibration_step
    single_calibration_step:
      models:
      - first_model
      first_model:
        type: first_model_type

For each calibration step, there are several parameters:

  • models - List of model names. Each model will then be defined in a parameter block defined by the name. Models define how to reproject observation points into the fixed frame. The basic model is a kinematic chain. Additional models can reproject through a kinematic chain and then a sensor, such as a 3d camera. Once loaded, models will be used by the error blocks to compute the reprojection errors between different sensor observations.
  • free_params - Defines the names of single-value free parameters. These can be the names of a joint for which the joint offset should be calculated, camera parameters such as focal lengths or the driver offsets for Primesense devices. If attempting to calibrate the length of a robot link, use free_frames to define the axis that is being calibrated.
  • free_frames - Defines the names of multi-valued free parameters that are 6-d transforms. Also defines which axis are free. X, Y, and Z can all be independently set to free parameters. Roll, pitch and yaw can also be set free, however it is important to note that because calibration internally uses an angle-axis representation, either all 3 should be set free, or only one should be free. You should never set two out of three to be free parameters.
  • free_frames_initial_values - Defines the initial offset values for free_frames. X, Y, Z offsets are in meters. ROLL, PITCH, YAW are in radians. This is most frequently used for setting the initial estimate of the checkerboard position, see details below.
  • error_blocks - List of error block names, which are then defined under their own namespaces.

For each model, the type must be specified. The type should be one of:

  • chain3d - Represents a kinematic chain from the base_link to the frame parameter (which in MoveIt/KDL terms is usually referred to as the tip).
  • camera3d - Represents a kinematic chain from the base_link to the frame parameter, and includes the pinhole camera model parameters (cx, cy, fx, fy) when doing projection of the points. This model only works if your sensor publishes CameraInfo. Further, the calibration obtained when this model is used and any of the pinhole parameters are free parameters is only valid if the physical sensor actually uses the CameraInfo for 3d projection (this is generally true for the Primesense/Astra sensors).
  • camera2d - Similar to camera3d, but for a 2d finder. Currently only works with the output of the CheckerboardFinder2d.

For each error block, the type must be specified. In addition to the type parameter, each block will have additional parameters:

  • chain3d_to_chain3d - The most commonly used error block type. This error block can compute the difference in reprojection between two 3D "sensors" which tell us the position of certain features of interest. Sensors might be a 3D camera or an arm which is holding a checkerboard. Was previously called "camera3d_to_arm":
    • model_a - First chain3d or camera3d model to use in computing reprojection error.
    • model_b - Second chain3d or camera3d model to use in computing reprojection error.
  • chain3d_to_camera2d- Currently only used for the CheckerboardFinder2d:
    • model_2d - camera2d model to use in computing reprojection error.
    • model_3d - chain3d or camera3d model to use in computing reprojection error.
    • scale - Scalar to multiply summed error by - note that error computed in this block is in pixel space, rather than metric space like most other error blocks.
  • chain3d_to_mesh - This error block type can compute the closeness between projected 3d points and a mesh. The mesh must be part of the robot body. This is commonly used to align the robot sensor with the base of the robot, using points that were found by the RobotFinder plugin:
    • model - chain3d or camera3d model to use in computing reprojection error.
    • link_name -Name of the link in the URDF for which mesh to use.
  • chain3d_to_plane - This error block can be used to compare projected points to a plane. Each observation point is reprojected, then the sum of distance to plane for each point is computed. The most common use case is making sure that the ground plane a robot sees is really on the ground:
    • model - The camera3d model for reprojection.
    • a, b, c, d - Parameters for the desired plane equation, in the form ax + by + cz + d = 0.
    • scale - Since the error computed is a distance from the plane over many points, scaling the error relative to other error blocks is often required.
  • plane_to_plane - This error block is able to compute the difference between two planes. For instance, 3d cameras may not have the resolution to actually see a checkerboard, but we can align important axis by making sure that a wall seen by both cameras is aligned. For each observation, the points are assumed to form a plane:
    • model_a - First chain3d or camera3d model to use in computing reprojection error.
    • model_b - Second chain3d or camera3d model to use in computing reprojection error.
    • normal_scale - The normal error is computed as the difference between the two plane normals and then multiplied by this scalar.
    • offset_scale - The offset error is computed as the distance from the centroid of the first plane to the second plane and then multiplied by this scalar.
  • outrageous - Sometimes, the calibration is ill-defined in certain dimensions, and we would like to avoid one of the free parameters from becoming absurd. An outrageous error block can be used to limit a particular parameter:
    • param - Free parameter to monitor.
    • joint_scale - If param is a joint name, multiply the free param value by this scalar.
    • position_scale - If param is a free frame, multiply the metric distance in X, Y, Z by this scalar.
    • rotation_scale - If param is a free frame, multiply the angular distance of the free parameter value by this scalar.

Calibration Poses

The final piece of configuration is the actual poses from which the robot should capture data. This YAML file can be created by running the capture_poses script. You will be prompted to move the robot to the desired pose and press ENTER, when done collecting all of your poses, you can type EXIT. This will create calibration_poses.yaml which is an array of capture poses:

- features: []
  joints:
  - first_joint
  - second_joint
  positions:
  - -0.09211555123329163
  - 0.013307283632457256
- features: []
  joints:
  - first_joint
  - second_joint
  positions:
  - -1.747204065322876
  - -0.07186950743198395

By default, every finder is used for every capture pose. In some cases, you might want to specify specific finders by editing the features:

# This sample pose uses only the `ground_plane_finder` feature finder
- features:
  - ground_plane_finder
  joints:
  - first_joint
  - second_joint
  positions:
  - -0.09211555123329163
  - 0.013307283632457256
# This sample pose will use all features
- features: []
  joints:
  - first_joint
  - second_joint
  positions:
  - -1.747204065322876
  - -0.07186950743198395

Checkerboard Configuration

When using a checkerboard, we need to estimate the transformation from the the tip of the kinematic chain to the virtual checkerboard frame. Calibration will be faster and more accurate if the initial estimate of this transformation is close to the actual value, especially with regards to rotation.

The simplest way to check your initial estimate is to run the calibration with only the six DOF of the checkerboard as free parameters. The output values will be the X, Y, Z, and A, B, C of the transformation. It is important to note that A, B, C are NOT roll, pitch, yaw -- they are the axis-magnitude representation. To get roll, pitch and yaw, run the to_rpy tool with your values of A, B, and C:

ros2 run robot_calibration to_rpy A B C

This will print the ROLL, PITCH, YAW values to put in for initial values. Then insert the values in the calibration.yaml:

free_frames_initial_values:
- checkerboard
checkerboard_initial_values:
  x: 0.0
  y: 0.225
  z: 0
  roll: 0.0
  pitch: 1.571
  yaw: 0.0

This tool can be helfpul for creating checkerboards.

Exported Results

The exported results consist of an updated URDF file, and one or more updated camera calibration YAML files. By default, these files will by exported into the /tmp folder, with filenames that include a timestamp of generation. These files need to be installed in the correct places to be properly loaded.

The fetch_calibration package has an example python script for installing the updated files.

Within the updated URDF file, there are two types of exported results:

  • Changes to free_frames are applied as offsets in the joint origins.
  • Changes to free_params (joint offsets) are applied as "calibration" tags in the URDF. In particular, they are applied as "rising" tags. These should be read by the robot drivers so that the offsets can be applied before joint values are used for controllers. The offsets need to be added to the joint position read from the device. The offset then typically needs to be subtracted from the commanded position sent to the device.

If your robot does not support the "calibration" tags, it might be possible to use only free_frames, setting only the rotation in the joint axis to be free.

The base_calibration_node

To run the base_calibration_node node, you need a somewhat open space with a large (~3 meters wide) wall that you can point the robot at.

Starting with the 0.10 release of robot_calibration, the actual movements the robot does can be programmed via the calibration_steps parameter:

  • calibration_steps - should be a list of string names of calibration steps to run.
  • step_name/type - should be either spin or rollout.
  • step_name/velocity - velocity to move the robot. This will be interpreted as angular velocity for spin steps and linear velocity for rollout steps.
  • step_name/rotations - only valid for spin steps. Number of rotations to complete at given velocity.
  • step_name/distance - only valid for rollout steps. Distance in meters, to rollout the robot.

Additional parameters:

  • accel_limit - acceleration limit for spin steps (radians/second^2).
  • linear_accel_limit - acceleration limit for rollout steps (meters/second^2).
  • min_angle/max_angle how much of the laser scan to use when measuring the wall angle (radians).

Node topics:

  • /odom - the node subscribes to this odom data. Message type is nav_msgs/Odometry.
  • /imu - the node subscribes to this IMU data. Message type is sensor_msgs/IMU.
  • /base_scan - the node subscribes to this laser data. Message type is sensor_msgs/LaserScan.
  • /cmd_vel - the node publishes rotation commands to this topic, unless manual mode is enabled. Message type is geometry_msgs/Twist.

The output of the node is a series of scalars to apply (where a value of 1.0 means there is currently no error):

[base_calibration_node-1] track_width_scale: 0.986743
[base_calibration_node-1] imu_scale: 0.984465
[base_calibration_node-1] rollout_scale: 0.981911

The application of these values is largely dependent on the drivers being used for the robot. For robots using ros_control or robot_control there is a track_width parameter typically supplied as a ROS parameter in your launch file.

Note: in ROS 1, these scalars were pre-multiplied by the existing track_width or imu_scale - however, with the lack of a unified parameter server in ROS 2, this is no longer done.

The magnetometer_calibration node

The magnetometer_calibration node records magnetometer data and can compute the hard iron offsets. After calibration, the magnetometer can be used as a compass (typically by piping the data through imu_filter_madgwick and then robot_localization).

Node parameters:

  • ~rotation_manual - if set to true, the node will not publish command velocities and the user will have to manually rotate the magnetometer. Default: false.
  • ~rotation_duration - how long to rotate the robot, in seconds.
  • ~rotation_velocity - the yaw velocity to rotate the robot, in rad/s.

Node topics:

  • /imu/mag - the node subscribes to this magnetometer data. Message type is sensor_msgs/MagneticField.
  • /cmd_vel - the node publishes rotation commands to this topic, unless manual mode is enabled. Message type is geometry_msgs/Twist.

The output of the calibration is three parameters, mag_bias_x, mag_bias_y, and mag_bias_z, which can be used with the imu_filter_madgwick package.

Migrating from ROS1

There are a number of changes in migrating from ROS1 to ROS2. Some of these are due to differences in the ROS2 system, others are to finally cleanup mistakes made in earlier version of robot_calibration.

The chains, models, free_frames and features parameters used to be lists of YAML dictionaries. That format is not easily supported in ROS2 and so they are now lists of string names and the actual dictionaries of information appear under the associated name. For instance, in ROS1, you might have:

models:
 - name: arm
   type: chain
   frame: wrist_roll_link
 - name: camera
   type: camera3d
   frame: head_camera_rgb_optical_frame

In ROS2, this becomes:

models:
- arm
- camera
arm:
  type: chain3d
  frame: wrist_roll_link
camera:
  type: camera3d
  frame: head_camera_rgb_optical_frame

NOTE: the "chain" type has been renamed "chain3d" in ROS2 for consistency (and to allow a future chain2d).

Multi-step calibration is now fully supported. A new parameter, calibration_steps must be declared as a list of step names. The models and free parameters are then specified for each step. As an example:

calibration_steps:
- first_calibration_step
- second_calibration_step
first_calibration_step:
  models: ...
  free_params: ...
second_calibration_step:
  models: ...
  free_params: ...

The capture poses can now be specified as YAML. The convert_ros1_bag_to_yaml script can be run in ROS1 to export your ROS1 bagfile as a YAML file that can be loaded in ROS2.