Camera IMU calibration

The camera-imu calibration tool estimates the spatial and temporal parameters of a camera system to a intrinsically calibrated IMU. Image and IMU data is provided as a ROS bag containing data streams for all cameras and the IMU.

The calibration parameters are estimated in a full batch optimization using splines to model the pose of the camera-imu rig. Detailed information about the approach can be found in the following papers: (see 1, 2)

##How to use it

###1) Requirements The intrinsic parameters of the IMU (e.g. scales, axis misalignment, nonlinearities,...) need to be calibrated beforehand and and its correction applied to the dataset.

Further an IMU configuration YAML has to be created containing the following statistical properties for the accelerometer and gyroscopes:

• noise density
• bias random walk

Refer to the YAML formats page for the data format.

###2) 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 list of image files and IMU data in a CSV file.

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

Good results have been obtained by using a camera rate of 20 Hz and an IMU rate of 200 Hz.

Tips:

• try to excite all six IMU axis evenly (rotation and translation)
• avoid shocks
• keep the motion blur low:
  • low shutter times
  • good illumination

WARNING: If you are using a calibration target with symmetries (checkerboard, circlegrid), movements which could lead to flips in the target pose estimates have to be avoided. To avoid this problem entirely the use of an Aprilgrid is recommended.

###3) 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

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

• report-imucam-%BAGNAME%.pdf: Report in PDF format. Contains all plots for documentation.
• results-imucam-%BAGNAME%.txt: Result summary as a text file.
• camchain-imucam-%BAGNAME%.yaml: Results in YAML format. This file is based on the input camchain.yaml with added transformations (and time shifts) for all cameras with respect to the imu. Please check the used format here.

##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.

kalibr_calibrate_cameras --bag dataset_icc.bag --cam camchain.yaml --imu imu0.yaml --target aprilgrid_6x6.yaml

References

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

1. 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.
2. 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.