whycode-ros2-interfaces

ROS2 interfaces package for the WhyCode localisation system.

Messages

MarkerArray.msg

Field	Type	Description
header	std_msgs/Header	Standard ROS header
markers	whycode_interfaces/Marker[]	Array of detected markers

Marker.msg

Field	Type	Description
id	int8	Marker ID
size	int32	Size in pixels
u	float32	Image-space X coordinate
v	float32	Image-space Y coordinate
angle	float32	Rotation around the surface normal
position	geometry_msgs/Pose	3D pose of the marker
rotation	geometry_msgs/Vector3	Euler angles: roll, pitch, yaw

Discovery.msg

Field	Type	Description
data	string	Not used. Only for presence signaling to the RQt GUI

Services

SelectMarker.srv

Request:

Field	Type	Description
point	geometry_msgs/Point	Selected point in image (x, y)

Response:

Field	Type	Description
success	bool	True if handled

SetCalibMethod.srv

Request Constants:

Name	Type	Value	Description
AUTO	int8	0	Automatic calibration
MANUAL	int8	1	Manual calibration

Request:

Field	Type	Description
method	int8	Calibration method type

Response:

Field	Type	Description
success	bool	Whether it succeeded
msg	string	Optional status message

SetCalibPath.srv

Request Constants:

Name	Type	Value	Description
SAVE	int8	0	Save current calibration
LOAD	int8	1	Load calibration from file

Request:

Field	Type	Description
action	int8	Save or Load action
path	string	Path to the YAML file

Response:

Field	Type	Description
success	bool	Whether the action succeeded
msg	string	Status or error message

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License

The code is avaiable only for non-commercial research and educational purposes only, see the LICENSE file for details. In any other case or when You are not sure about licensing, please, contact us!

If you use this localization system for your research, please don't forget to cite at least one relevant paper below.

Where is it described ?

WhyCon was first presented on International Conference on Advanced Robotics 2013, later in the Journal of Intelligent and Robotics Systems and finally at the Workshop on Open Source Aerial Robotics during the International Conference on Intelligent Robotic Systems, 2015. Its early version was also presented at the International Conference of Robotics and Automation, 2013. An extension of the system, which used a necklace code to add ID's to the tags, achieved a best paper award at the SAC 2017 conference. Improved version with the full 6DOF estimation was presented in the Applied Computing Review, 2023. If you decide to use this software for your research, please cite relevant papers provided below.

References

1. J. Ulrich et al.: Real time fiducial marker localisation system with full 6 DOF pose estimation. ACM SIGAPP Applied Computing Review, 2023. [bibtex].
2. J. Ulrich et al.: Towards fast fiducial marker with full 6 DOF pose estimation. Symposium on Applied Computing, 2022. [bibtex].
3. J. Ulrich: Fiducial marker-based multiple camera localisation system. Master's thesis. Czech Technical University in Prague, 2022. [bibtex].
4. K. Zampachu: Visual analysis of beehive queen behaviour. Bachelor's thesis. Czech Technical University in Prague, 2022. [bibtex].
5. J. Ulrich: Fiducial Marker Detection for Vision-Based Mobile Robot Localisation. Bachelor's thesis. Czech Technical University in Prague, 2020. [bibtex].
6. P. Lightbody, T. Krajník et al.: An Efficient Visual Fiducial Localisation System. Applied Computing Review, 2017.
7. P. Lightbody, T. Krajník et al.: A versatile high-performance visual fiducial marker detection system with scalable identity encoding Symposium on Applied Computing, 2017.
8. M. Nitsche, T. Krajník et al.: WhyCon: An Efficent, Marker-based Localization System. IROS Workshop on Open Source Aerial Robotics, 2015.
9. T. Krajník, M. Nitsche et al.: A Practical Multirobot Localization System. Journal of Intelligent and Robotic Systems (JINT), 2014.
10. T. Krajník, M. Nitsche et al.: External localization system for mobile robotics. International Conference on Advanced Robotics (ICAR), 2013.
11. J. Faigl, T. Krajník et al.: Low-cost embedded system for relative localization in robotic swarms. International Conference on Robotics and Automation (ICRA), 2013.

Acknowledgements

The development of this work is currently supported by the EU FET Open programme under grant agreement No.964492 project RoboRoyale. The development of this work was supported by the Czech Science Foundation project 17-27006Y STRoLL. In the past, the work was supported by EU within its Seventh Framework Programme project ICT-600623 STRANDS. The Czech Republic and Argentina have given support through projects 7AMB12AR022, ARC/11/11 and 13-18316P.