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Details

The imu_filter_madgwick package is used to filter and fuse raw data from IMU devices. It fuses angular velocities, accelerations, and (optionally) magnetic readings from a generic IMU device into an orientation quaternion, and publishes the fused data on the imu/data topic. The package has been tested using the raw data output of a Phidgets IMU (Spatial 3/3/3) device. The package is a wrapper of Sebastian Madgwick's IMU filter [1].

Supported devices

The filter should be general enough to use with any IMU which publishes angular velocities and linear accelerations (and optionally magnetic field).

It has been tested with:

Phidgets Spatial 3/3/3

Example

Here's an example of the non-ROS version of the code, courtesy of Sebastian Madgwick.

Todo: similar video from rviz.

ROS API

Two drivers are available: imu_filter_node and imu_filter_nodelet. Their parameters and topics are identical.

Subscribed Topics

imu/data_raw (sensor_msgs/Imu)

• Message containing raw IMU data, including angular velocities and linear accelerations.

imu/mag (sensor_msgs/MagneticField)

• [optional] Magnetic field vector; the type is either sensor_msgs/MagneticField or geometry_msgs/Vector3Stamped (deprecated), depending on the parameter use_magnetic_field_msg (see below)

Published Topics

imu/data (sensor_msgs/Imu)

• The fused Imu message, containing the orientation.

Parameters

Dynamically Reconfigurable Parameters

See the dynamic_reconfigure package for details on dynamically reconfigurable parameters.

~gain (double, default: 0.1)

• Gain of the filter. Higher values lead to faster convergence but more noise. Lower values lead to slower convergence but smoother signal. Range: 0.0 to 1.0

~zeta (double, default: 0.0)

• Gyro drift gain (approx. rad/s). Range: -1.0 to 1.0

~mag_bias_x (double, default: 0.0)

• Magnetometer bias (hard iron correction), x component. Range: -10.0 to 10.0

~mag_bias_y (double, default: 0.0)

• Magnetometer bias (hard iron correction), y component. Range: -10.0 to 10.0

~mag_bias_z (double, default: 0.0)

• Magnetometer bias (hard iron correction), z component. Range: -10.0 to 10.0

~orientation_stddev (double, default: 0.0)

• Standard deviation of the orientation estimate. Range: 0.0 to 1.0

Not Dynamically Reconfigurable Parameters

~world_frame (string, default: "nwu" for Indigo - Kinetic, "enu" for Lunar+)

• The world frame with respect to which the orientation is indicated (see REP-145). For historic reasons, the old default is "nwu" (North-West-Up). New deployments should use "enu". Valid values: "nwu", "enu", "ned".

~use_mag (bool, default: true)

• Whether to use the magnetic field data in the data fusion.

~use_magnetic_field_msg (bool, default: false for Hydro and Indigo, true for Jade+)

• If set to true, subscribe to the /imu/mag topic as a sensor_msgs/MagneticField; if set to false (deprecated), use geometry_msgs/Vector3Stamped.

~fixed_frame (string, default: odom)

• The parent frame to be used in publish_tf.

~publish_tf (bool, default: true)

• Whether to publish a TF transform that represents the orientation of the IMU, using the frame specified in fixed_frame as the parent frame and the frame given in the input imu message as the child frame.

~reverse_tf (bool, default: false)

• If set to true, publish transforms from imu_frame to fixed frame instead of the other way around.

~constant_dt (double, default: 0.0)

• The dt to use; if 0.0 (default), compute dt dynamically from message headers.

~publish_debug_topics (bool, default: false)

• If set to true, publish a couple of debug topics.

~stateless (bool, default: false)

• If set to true, don't publish a filtered orientation. Instead, publish the stateless estimate of the orientation based on the latest accelerometer (and optionally magnetometer) readings alone. Useful for debugging.

~remove_gravity_vector (bool, default: false)

• If set to true, subtract the gravity vector from the acceleration field in the published IMU message.

References

[1] http://www.x-io.co.uk/open-source-imu-and-ahrs-algorithms/

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