Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints

Planar markers are useful in robotics and computer vision for mapping and localisation. Given a detected marker in an image, a frequent task is to estimate the 6DOF pose of the marker relative to the camera, which is an instance of planar pose estimation (PPE). Although there are mature techniques, PPE suffers from a fundamental ambiguity problem, in that there can be more than one plausible pose solutions for a PPE instance. Especially when localisation of the marker corners is noisy, it is often difficult to disambiguate the pose solutions based on reprojection error alone. Previous methods choose between the possible solutions using a heuristic criteria, or simply ignore ambiguous markers. We propose to resolve the ambiguities by examining the consistencies of a set of markers across multiple views. Our specific contributions include a novel rotation averaging formulation that incorporates long-range dependencies between possible marker orientation solutions that arise from PPE ambiguities. We analyse the combinatorial complexity of the problem, and develop a novel lifted algorithm to effectively resolve marker pose ambiguities, without discarding any marker observations. Results on real and synthetic data show that our method is able to handle highly ambiguous inputs, and provides more accurate and/or complete marker-based mapping and localisation.Comment: 7 pages, 4 figures, 4 table

Long range moiré patterns

Previous work has gone towards using moiré patterns formed with lenticular lenses to perform pose estimation for short ranges. This thesis investigates existing theory of moiré patterns, most notably the Fourier and first harmonic approximation models. This theory has been drawn upon to create a generalised model of planar moiré patterns in 3D. For example, those generated from two patterns with fine grids separated in space. This improves on previous research that does not investigate this specific kind of 3D pattern as closely. This thesis also developed a framework that simulates moiré patterns using this model. Along with this, the proposed framework can also solve pose information about a moiré pattern given an image. Experiments varying camera lateral translation were accurate for the close-range testing, with about 10 mm accuracy from a distance of 160 mm. Results from varying camera distance where 0–130 mm accuracy varied from ranges 100–2000 mm. A y-tilt estimation experiment was performed using the solver from this framework. At 3.116 m it was able to estimate an angle with an error of 5° for angles as wide as 30° and was able to estimate angles with an error of 0.25° for angles less than 5°. This is better than similar existing methods such as the Metria Moiré Phase Tracking marker’s maximum absolute errors of up to 2.8 mm and 2.1°