Camera IMU calibration

The camera-imu calibration tool estimates the spatial and temporal parameters of an imu/camera pair.

The image and imu data is provided as a ROS bag containing data streams for all cameras and the IMU. The calibration estimated in a full batch optimization using splines to model the pose of the camera-imu rig. Detailed information about the applied approach can be found in the following papers: (see 1, 2)

##How to use it

###1) Requirements The intrinsic parameters of the IMU such as

scales
axis misalignment
nonlinearities

need to be estimated and its corrected beforehand.

Further the following statistics need to be known:

noise density
bias random walk

This data has to be provided to the calibrator as an IMU-YAML file. Refer to this page for the data format.

###1) Collect images Create a ROS bag containing the raw image streams either by directly recording from a ROS sensor stream or by using the bagcreater script on a sequence of image files.

The calibration target is fixed in this calibration and the camera-imu system is moved in front of the target. Therefore it is important to have good illumination and to keep the cameras shutter times as low as possible to avoid motion blur while exciting the IMU.

Good results have been obtained by using a camera rate of 20 Hz while the IMU was recorded at 200 Hz.

Tips:

try to excite all six IMU axis evenly
avoid shocks
keep the motion blur low
- low shutter times
- good illumination
if you are using a calibration target with symmetries (checkerboard, circlegrid), try to avoid rotations over the symmetry, as this would lead to orientation flips in the

###2) Running the calibration The tool must be provided with the following input:

--bag filename.bag
ROS bag containing the image and IMU data
--cam camchain.yaml
intrinsic and extrinsic calibration parameters of the camera system. the output of the multiple-camera-calibration tool can be used directly here. (see YAML formats
--imu imu.yaml
contains the IMU statistics and the IMU's topic (see YAML formats)
--target target.yaml
the calibration target configuration (see Cailbration targets)

The calibration can then be run using:

kalibr_calibrate_imu_camera --bag [filename.bag] --cam [camchain.yaml] --imu [imu.yaml] --target [target.yaml]

The temporal calibration is turned off by default and can be enabled using the --time-calibration argument. More information about options is available using the help argument:<br>

kalibr_calibrate_imu_camera --h

###3) The output The calibration will produce the following output files:

report-%BAGNAME%.pdf: Report in PDF format. Contains all plots for documentation.
results-%BAGNAME%.txt: Result summary as a text file.
chain.yaml: Results in YAML format. This file can be used as an input for the camera-imu calibrator. Please see the here for the used format.

##An example run using a sample dataset Download the sample dataset here and extract it. The archive will contain the bag-file, calibration target and imu configuration file.

References

Please cite the appropriate papers when using this toolbox or parts of it in an academic publication.

Paul Furgale, Joern Rehder, Roland Siegwart (2013). Unified Temporal and Spatial Calibration for Multi-Sensor Systems. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan.
Paul Furgale, T D Barfoot, G Sibley (2012). Continuous-Time Batch Estimation Using Temporal Basis Functions. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2088–2095, St. Paul, MN.