Multiple camera calibration

#Multiple camera calibration with intrinsics This tool estimates the intrinsic and extrinsic parameters of a multiple camera-system with non-shared overlapping fields of view. The image data is provided as a ROS bag containing image streams for all cameras. The calibration routine will go through all images and select images using information theoretic measures in order to get a good estimate of the system parameters. (see 1)

Arbitrary combinations these camera and distortion models can be mixed in one calibration run. Have a look at this page for a list of the supported camera and distortion models.

##How to use?

###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.

It is advised to lower the frequency of the camera streams to around 4 Hz while capturing the calibration data trying to avoid gathering too many images containing almost redundant information. Which would unnecessarily increase the number of images to be processing by the calibration.

###2)Running the calibration This sectioownload the sample dataset here. bar

1. Collect a ros bag containing the calibration image streams
2. Run the calibration

rosrun aslam_camera_calibration calibrate --model pinhole-radtan pinhole-equi omni-radtan

###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: Results in TXT format.
• chain.yaml: Results in YAML format. This file can be used as an input for the camera-imu calibrator

##An example run using a sample dataset

References

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

1. J. Maye, P. Furgale, R. Siegwart (2013). Self-supervised Calibration for Robotic Systems, In Proc. of the IEEE Intelligent Vehicles Symposium (IVS)