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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 gyro and track width parameters by rotating the robot in place and tracking the actual rotation 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 frame which is fixed relative to the end effector of the arm. Within the virtual frame, we know the position of each point of the checkerboard corners.

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

Configuration is typically handled through two sets of YAML files. The first YAML file specifies the details needed for data capture:

• chains - The kinematic chains of the robot which should be controlled, and how to control them so that we can move the robot to each desired pose for sampling.
• features - The configuration for the various "feature finders" that will be making our observations at each sample pose. Current finders include an LED detector, checkerboard finder, and plane finder. Feature finders are plugin-based, so you can create your own.

The second 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 ROS2, 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.

For each calibration step, there are several parameters:

• models - Models define how to reproject points. The basic model is a kinematic chain. Additional models can reproject through a kinematic chain and then a sensor, such as a 3d camera. For IK chains, frame parameter is the tip of the IK chain. The "models" parameter is a list of model names.
• 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 other parameters, such as driver offsets for Primesense devices.
• 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 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).

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

• chain3d_to_chain3d - 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".
• chain3d_to_mesh - This error block 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.
• chain3d_to_plane - This error block can compute the difference between projected 3d points and a desired plane. The most common use case is making sure that the ground plane a robot sees is really on the ground.
• 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.
• 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.

Checkerboard Configuration

When using a checkerboard, we need to estimate the transformation from the the kinematic chain to the checkerboard. 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

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.

Example Configuration

The UBR-1 robot uses this package to calibrate in ROS2. Start with the calibrate_launch.py in ubr1_calibration package.

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. The robot should be pointed at the wall and it will then spin around at several different speeds. On each rotation it will stop and capture the laser data. Afterwards, the node uses the angle of the wall as measured by the laser scanner to determine how far the robot has actually rotated versus the measurements from the gyro and odometry. We then compute scalar corrections for both the gyro and the odometry.

Node parameters:

• <code>/base_controller/track_width</code> - this is the default track width.
• <code>/imu/gyro/scale</code> - this is the initial gyro scale.
• <code>~min_angle/~max_angle</code> how much of the laser scan to use when measuring the wall angle (radians).
• <code>~accel_limit</code> - acceleration limit for rotation (radians/second^2).

Node topics:

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

The output of the node is a new scale for the gyro and the odometry. 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.

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:

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

Node topics:

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

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

Status

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