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MPL Calibration Toolbox

VECtor Benchmark is the first complete set of benchmark datasets captured with a multi-sensor setup containing an event-based stereo camera, a regular stereo camera, multiple depth sensors, and an inertial measurement unit. The setup is fully hardware-synchronized and underwent accurate extrinsic calibration. All sequences come with ground truth data captured by highly accurate external reference devices such as a motion capture system. Individual sequences include both small and large-scale environments, and cover the specific challenges targeted by dynamic vision sensors.

This toolbox is a ROS workspace integrating with a set of easy-to-use calibration functions, including:

License and Citation

This toolbox, together with the MPL Dataset Toolbox and the k4a Projector, is available as open-source under the terms of the BSD-3-Clause-Clear License. If you use this toolbox in an academic context, please cite the publication as follows:

@Article{gao2022vector,
  author  = {Gao, Ling and Liang, Yuxuan and Yang, Jiaqi and Wu, Shaoxun and Wang, Chenyu and Chen, Jiaben and Kneip, Laurent},
  title   = {{VECtor}: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM},
  journal = {IEEE Robotics and Automation Letters},
  pages   = {8217--8224},
  volume  = {7},
  number  = {3},
  year    = {2022},
  doi     = {10.1109/LRA.2022.3186770}
}

Getting Started

The following instructions are tested on Ubuntu 20.04 with ROS Noetic, a ROS desktop-full installation is therefore required. On top of that, the following libraries (Eigen 3, OpenCV 4.2, Ceres Solver 1.14.0, yaml-cpp) should be installed from APT or you can just use rosdep to manage package dependencies as below.

cd ~/catkin_ws/src
git clone https://github.com/mgaoling/mpl_calibration_toolbox.git
cd ..
rosdep install --from-paths src --ignore-src -y # install dependencies
catkin_make
source ~/catkin_ws/devel/setup.bash

Camera Intrinsic Calibration

How to reproduce the result?

Download the intrinsic data bag from the Calibration Page on VECtor Benchmark, and place it into the data folder. Here, we use the right_event_camera_intrinsic_data.zip file as an example. Decompress the file by:

roscd mpl_calibration_toolbox/data
unzip right_event_camera_intrinsic_data.zip

Check and modify the parameters in the config/intrinsic_calibration.yaml, then launch the camera intrinsic calibration by:

roslaunch mpl_calibration_toolbox intrinsic_calibration.launch

Once the playback is over, click the calibrate button, and wait for the results to be displayed on the terminal.

How to calibrate by yourself?

Launch the ROS driver to publish data from the camera to ROS topics.
(Optional) If this is an event camera, install the MPL Dataset Toolbox, then launch the event visualization by:

roslaunch mpl_dataset_toolbox event_visualization.launch

Open the data/metavision_calibration_pattern_chessboard.html file on another screen. (Optional) Click the start button if this is an event camera.
Launch the ROS Camera Calibration toolbox by:

rosrun camera_calibration cameracalibrator.py --size 9x6 --square [square_length] image:=[image_topic] --no-service-check

Move the checkerboard around in the camera frame. Click the calibrate button whenever you have collected enough data, and wait for the results to be displayed on the terminal.

Note: It is recommended to double-check the corner extraction among all recorded images. Delete the unwanted images if necessary, and then reproduce the results for better accuracy.

IMU Intrinsic Calibration

How to reproduce the result?

Download the config file and the data bag from the Calibration Page on VECtor Benchmark.
Install the Allan Variance ROS toolbox, then launch the IMU intrinsic calibration by:

rosrun allan_variance_ros allan_variance imu_intrinsic_data_5hrs_recordings.bag imu_intrinsic_config.yaml
rosrun allan_variance_ros analysis.py --data allan_variance.csv

How to calibrate by yourself?

Please refer to the Allan Variance ROS toolbox for more details.

Joint Camera Extrinsic Calibration

How to reproduce the result?

Download the joint camera extrinsic data bag and all related intrinsic results from the Calibration Page on VECtor Benchmark, and place them into the data folder. Here, we use the small_scale_joint_camera_extrinsic_data.zip file as an example. Decompress the file by:

roscd mpl_calibration_toolbox/data
unzip small_scale_joint_camera_extrinsic_data.zip

Check and modify the parameters in the config/joint_extrinsic_calibration.yaml, then launch the joint camera extrinsic calibration by:

roslaunch mpl_calibration_toolbox joint_extrinsic_calibration.launch

How to calibrate by yourself?

Launch the ROS driver to publish data from the camera to ROS topics.
(Optional) If this is an event camera, install the MPL Dataset Toolbox, then launch the event visualization by:

roslaunch mpl_dataset_toolbox event_visualization.launch

Open the data/metavision_calibration_pattern_chessboard.html file on another screen.
Run rqt in another terminal to display all the regular image frames, along with accumulated event frames if exist.
Hold the sensor suite still in one place, save all the frames as images and place them into a folder following the same format as in small_scale_joint_camera_extrinsic_data. Repeat this process ten to twenty times at various places.
Check and modify the parameters in the config/joint_extrinsic_calibration.yaml, then launch the joint extrinsic camera calibration by:

roslaunch mpl_calibration_toolbox joint_extrinsic_calibration.launch

Camera-IMU Extrinsic Calibration

How to reproduce the result?

Download and decompress the config file and the data bag from the Calibration Page on VECtor Benchmark.
Install the Kalibr toolbox, then launch the Camera-IMU extrinsic calibration by:

kalibr_calibrate_imu_camera --bag [data.bag] --cam cam.yaml --imu imu.yaml --target apriltag.yaml --timeoffset-padding 0.1

How to calibrate by yourself?

Please refer to the WIKI page on Kalibr toolbox for more details.

Camera-MoCap Hand-eye Calibration

How to reproduce the result?

Download the Camera-MoCap extrinsic data bag and all related intrinsic results from the Calibration Page on VECtor Benchmark, and place them into the data folder.
Check and modify the parameters in the config/camera_mocap_calibration.yaml, then launch the Camera-MoCap extrinsic calibration by:

roslaunch mpl_calibration_toolbox camera_mocap_calibration.launch

How to calibrate by yourself?

Please refer to the statement from the Calibration Page on VECtor Benchmark. Note: Synchronization between the camera system and the MoCap system is rather crucial in order to achieve better accuracy.