The AprilTag3 MEX wrapper for MATLAB on Linux implements tag detection and 3–D pose estimation.
N.B.: Only the tag family ''tag36h11'' is supported!
Please cite this software if you use MATLAB_AprilTag3 in your research:
[1] A. A. Díaz Salazar, "MATLAB_AprilTag3", Linköping, Sweden, 2019. Online.
Read PROCEDURE.md for install instructions.
The MEX function "apriltags.c" implements AprilTag3 detection and 3–D pose estimation (April 2019 version) and contains a MAJOR FIX to prevent deallocation of uninitialized memory.
AprilRobotics guide: https://github.com/AprilRobotics/apriltag/wiki/AprilTag-User-Guide
Run help apriltags for syntax details.
im_rgb = imread("my_RGB_image.png");
IM = rgb2gray(im_rgb);
tags = apriltags(IM, TAGSIZE, K);Inputs:
IM is a grayscale image.
TAGSIZE specifies the actual (printed!) tag size in meters.
K specifies camera calibration as a 3x3 matrix defined by K = [fx 0 u0; 0 fy v0; 0 0 1] where (fx, fy) is the focal length and (u0, v0) is the principal point (optical center).
Outputs:
tags is a vector of structures with the parameters of the AprilTags found in IM.
Basic MEX infrastructure by Peter Corke given in the Machine Vision Toolbox for MATLAB.
MEX fix provided by Gustaf Hendeby.
[1] A. A. Díaz Salazar, "MATLAB_AprilTag3", Linköping, Sweden, 2019. Online.
[2] M. Krogius, A. Haggenmiller, and E. Olson, “Flexible Layouts for Fiducial Tags”, In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019, pp. 1898–1903. DOI: 10.1109/IROS40897.2019.8967787.
[3] P. Corke, The Machine Vision Toolbox: a MATLAB toolbox for vision and vision-based control, In: IEEE Robotics and Automation Magazine, vol. 12(4), 2005, pp. 16–25. DOI: 10.1109/MRA.2005.1577021.
[4] P. Corke, MATLAB toolboxes: robotics and vision for students and teachers, In: IEEE Robotics and Automation Magazine, vol. 14(4), 2007, pp. 16–17. DOI: 10.1109/M-RA.2007.912004.