Generation of fiducial marker dictionaries using Mixed Integer Linear Programming
Square based fiducial markers are one of the most popular approaches for camera pose estimation due to its fast detection and robustness. In order to maximize their error correction capabilities, it is required to use an inner binary codification with a large inter marker distance. This paper proposes two Mixed Integer Linear Programming (MILP) approaches to generate configurable square based fiducial marker dictionaries maximizing their inter marker distance. The first approach guarantees the optimal solution, however, it can only be applied to relatively small dictionaries and number of bits since the computing times are too long for many situations. The second approach is an alternative formulation to obtain suboptimal dictionaries within restricted time, achieving results that still surpass significantly the current state of the art methods. HighlightsThe paper proposes two methods to obtain fiducial markers based on the MILP paradigm.First model guarantees the optimality in terms of inter marker distance.Second model generates suboptimal markers within restricted time.The markers generated allow the correction of a higher amount of erroneous bits.
Pattern Recognit.