We report a method for the detection and recognition of a large planar mirror based on the images captured by a monocular camera. We start with deriving a mirror transformation matrix in a homogeneous coordinate and geometric constraints for corresponding real and virtual feature points in the image. We find that existing feature detection methods are not reflection invariant. We introduce a secondary artificial reflection to virtual features to generate secondary features which are proven to share a rigid body motion relationship with the original feature set. We propose an iterative strategy to adjust the secondary mirror configuration so that existing feature matching methods can be used. The combined method yields a robust mirror detection algorithm which has been verified in physical experiments. 2011 IEEE.
2011 IEEE International Conference on Robotics and Automation
2011 IEEE International Conference on Robotics and Automation
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