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#671 implements pose from RGBD to try to overcome this issue, but you need an RGBD sensor and the quality of the depth map, the range of the depth sensor, the extrinsic calibration between color and depth sensors will matter.
Is it possible to detect these problematic situations? Is something similar to the Lowe ratio test (if the reprojection error for the two poses are too close, maybe we can say that the computed pose can be ambiguous?) possible to detect this situation?
A video to see the pose flipping issue (warning: in the video I was using some arbitrary focal length values (it was to illustrate marker detection and pose computation), that was probably accentuating the problem):
This thread is to document the pose ambiguity issue that can arise with planar pose computation.
Visually:
from Iterative Pose Estimation Using Coplanar Feature Points, Denis Oberkampf, Daniel F.DeMenthon, Larry S.Davis, 1995:

from Sensor Fusion for Fiducial Tags: Highly Robust Pose Estimation from Single Frame RGBD, Pengju Jin, Pyry Matikainen, Siddhartha S. Srinivasa, 2017:

from the ArUco FAQ:

#671 implements pose from RGBD to try to overcome this issue, but you need an RGBD sensor and the quality of the depth map, the range of the depth sensor, the extrinsic calibration between color and depth sensors will matter.
@fspindle @chaumett
Is it possible to detect these problematic situations? Is something similar to the Lowe ratio test (if the reprojection error for the two poses are too close, maybe we can say that the computed pose can be ambiguous?) possible to detect this situation?
Or the only easy solution is to use two non-planar tags (similar to http://www.particlerobots.com/luismateos/latching/apriltags3d.html)?
A video to see the pose flipping issue (warning: in the video I was using some arbitrary focal length values (it was to illustrate marker detection and pose computation), that was probably accentuating the problem):
underwater_tags_detection_half.mp4