robot_calibration repository

Repository Summary

Checkout URI https://github.com/mikeferguson/robot_calibration.git
VCS Type git
VCS Version master
Last Updated 2020-06-02
Dev Status MAINTAINED
CI status No Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
robot_calibration 0.6.3
robot_calibration_msgs 0.6.3

README

Robot Calibration

This package offers ROS 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:

  • calibrate_base - 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.
  • feature_finders - 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:

  • 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.
  • 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 - These define the actual errors to compare during optimization. There are several error blocks available at this time:
    • 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.
    • 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:

rosrun 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:
 - name: checkerboard
   x: 0.0
   y: 0.225
   z: 0
   roll: 0.0
   pitch: 1.571
   yaw: 0.0

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 calibrate_base node

To run the calibrate_base 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:

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

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 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:

  • ~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.

Status

  • Kinetic Devel Job Status: Build Status
  • Kinetic AMD64 Debian Job Status: Build Status
  • Melodic Devel Job Status: Build Status
  • Melodic Coverage: codecov

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/mikeferguson/robot_calibration.git
VCS Type git
VCS Version master
Last Updated 2020-06-02
Dev Status MAINTAINED
CI status No Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
robot_calibration 0.6.3
robot_calibration_msgs 0.6.3

README

Robot Calibration

This package offers ROS 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:

  • calibrate_base - 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.
  • feature_finders - 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:

  • 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.
  • 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 - These define the actual errors to compare during optimization. There are several error blocks available at this time:
    • 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.
    • 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:

rosrun 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:
 - name: checkerboard
   x: 0.0
   y: 0.225
   z: 0
   roll: 0.0
   pitch: 1.571
   yaw: 0.0

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 calibrate_base node

To run the calibrate_base 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:

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

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 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:

  • ~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.

Status

  • Kinetic Devel Job Status: Build Status
  • Kinetic AMD64 Debian Job Status: Build Status
  • Melodic Devel Job Status: Build Status
  • Melodic Coverage: codecov

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/mikeferguson/robot_calibration.git
VCS Type git
VCS Version indigo-devel
Last Updated 2018-02-13
Dev Status MAINTAINED
CI status No Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
robot_calibration 0.5.5
robot_calibration_msgs 0.5.5

README

Robot Calibration

This package offers calibration of 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.

Overview

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.
  • feature_finders - The configuration for the various "feature finders" that will be making our observations at each sample pose. Current finders include an LED detector and a checkerboard finder.

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

  • 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 camera.
  • 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.
  • error_blocks - These define the actual errors to compare during optimization. There are several error blocks available at this time:
    • camera3d_to_arm - This error block can compute the difference in reprojection between a 3D camera and a kinematic chain which is holding the projected points.
    • 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.

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.

Status

  • Devel Job Status: Build Status
  • AMD64 Debian Job Status: Build Status

CONTRIBUTING

No CONTRIBUTING.md found.

Repository Summary

Checkout URI https://github.com/mikeferguson/robot_calibration.git
VCS Type git
VCS Version indigo-devel
Last Updated 2018-02-13
Dev Status MAINTAINED
CI status No Continuous Integration
Released RELEASED
Package Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Packages

Name Version
robot_calibration 0.5.5
robot_calibration_msgs 0.5.5

README

Robot Calibration

This package offers calibration of 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.

Overview

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.
  • feature_finders - The configuration for the various "feature finders" that will be making our observations at each sample pose. Current finders include an LED detector and a checkerboard finder.

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

  • 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 camera.
  • 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.
  • error_blocks - These define the actual errors to compare during optimization. There are several error blocks available at this time:
    • camera3d_to_arm - This error block can compute the difference in reprojection between a 3D camera and a kinematic chain which is holding the projected points.
    • 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.

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.

Status

  • Devel Job Status: Build Status
  • AMD64 Debian Job Status: Build Status

CONTRIBUTING

No CONTRIBUTING.md found.