kr_attitude_eskf is an implementation of the Error-State Kalman Filter described in:

• “Attitude Error Representations for Kalman Filtering” - F. Landis Markley

• 0.0.8:

• Removed the reference calib option, which produced somewhat poor results anyways.
• Mag calib code now uses either float or double.
• Removed kr_math, eigen_conversions dependencies.

• 0.0.7:
  • AttitudeESKF accepts matrices for covariance parameters, instead of assuming diagonal noise.
  • Noise parameters are not provided by rosparam anymore, and are expected to arrive in the incoming messages.
  • Process model treats gyro covariance with the correct units (rad/s)^2 instead of (rad)^2.
  • Magnetic bias/scale/reference vectors are loaded as lists instead of key-value arrays.
  • Magnetometer calibration now uses least squares for initial guess, and LM iteration is robustified with Cauchy weighting.
  • Calibration is triggered by a callable ROS service, instead of from the launch file options.
  • Added support for diagnostic_updater.
  • Renamed adjusted_field topic to corrected_field.
  • Refactored ROS code into a Node class.
  • Removed broadcast_frame option and tf-dependent code.
  • Pose is always published.
• 0.0.6:
  • Added publish_pose option.
• 0.0.5:
  • First public release.

This implementation utilizes accelerometer, gyroscope, and (optionally) the magnetometer in order to provide an attitude estimate. Gyroscope biases are estimated online with a moving average filter.

A 4th order Runge Kutta process model is employed, along with a classical EKF update - save for the error-state modifications. Internally, the filter uses a quaternion parameterization of SO(3), thereby avoiding the uglier qualities of euler angles.

The core ESKF code depends on Eigen. The ROS node + magnetometer calibrator depends on kr_math also.

The ROS implementation exposes several parameters:

When using the node, you should remap ~imu and ~field to the appropriate topics. See attitude_eskf.launch for an example.

The following topics are always published:

If enable_magnetometer is set, the following topics will also be published:

This node includes a built-in magnetometer calibration mode. Calibration is started by calling the begin_calibration service. You will observe a log message on rosout indicating that calibration has started. Steps:

1. Rotate the device about the world z axis 360 degrees.
2. Rotate the device 90 degrees about the roll/pitch axis, and then again 360 degrees about the world z axis.
3. You should see the resulting parameters printed on rosout. They will also be saved to rosparam.
4. After calibration, enable_magnetometer will be set to true and the magnetic field will be included in the estimate.

• It is assumed that all IMU topics are strictly synchronized.
• It is assumed that all IMU sensors are physically aligned.
• The default value of mag_calib/reference is not valid. You must either calibrate the device, or set a known value using a magnetic model (such as IGRF11).

Please report issues to the official package maintainer.

• Early version running on iOS
• Avik De has used this filter on STM32 w/ MPU6000: Details
• Video of a pico-quadrotor using ESKF