Camera IMU calibration

This tool calibrates the spatial and temporal parameters of an imu/camera pair.

Detailed information about the used approach is available in the following papers: 1, 2

##How to use it The following instructions briefly explain the calibration process.

1. Collect calibration data either as

• a ROS bag (with image and imu topics)
• a set of images and IMU data as a CSV file<br> Please see the sample datasets for the data format.

2. Create an IMU, camera and target configration yaml-file.<br>
3. IMU.yaml: <br>
   • IMU error statistics (default values should work with common MEMS IMUs)<br>
   • rostopic for imu data<br>
4. cam.yaml: <br>
   • camera intrinsic calibration<br>
   • rostopic for image data<br>
5. target.yaml: <br>
   • target type<br>
   • target dimensions<br>

Templates can be found in the yaml_example folder of the imu_camera_calibration ROS package.

3. Run the calibration with

• ROS bag:<br>

  rosrun imu_camera_calibration calib_term.py --imu IMU.yaml --cam cam.yaml \
         --target target.yaml --bag mybag.bag
  

• Images/CSV:<br>

  rosrun imu_camera_calibration calib_term.py --imu IMU.yaml --cam cam.yaml \
         --target target.yaml --csv image.csv imu.csv
  

To observe the target corner extraction and get result plots you can add the --show-all flag.

More information is available using the help argument:<br> rosrun imu_camera_calibration calib_term.py --h

##Tips

• try to excite all six IMU axis evenly
• avoid shocks
• keep the motion blur low
  • low shutter times
  • good illumination
• avoid too flat angles between the camera axis and the calibration target
• hide external apriltags, if the aprilgrid is used
• 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

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.