Mapping and Localization from Planar Markers

TL;DR

This paper introduces a robust method for mapping and localizing with large sets of planar markers, outperforming keypoint-based approaches in accuracy and robustness under challenging conditions.

Contribution

A novel approach that combines pose graph correction and global optimization for mapping and localization using squared planar markers.

Findings

Outperforms Structure from Motion in experiments

Provides robust localization under rapid motion and viewpoint changes

Effective in large-scale marker mapping scenarios

Abstract

Squared planar markers are a popular tool for fast, accurate and robust camera localization, but its use is frequently limited to a single marker, or at most, to a small set of them for which their relative pose is known beforehand. Mapping and localization from a large set of planar markers is yet a scarcely treated problem in favour of keypoint-based approaches. However, while keypoint detectors are not robust to rapid motion, large changes in viewpoint, or significant changes in appearance, fiducial markers can be robustly detected under a wider range of conditions. This paper proposes a novel method to simultaneously solve the problems of mapping and localization from a set of squared planar markers. First, a quiver of pairwise relative marker poses is created, from which an initial pose graph is obtained. The pose graph may contain small pairwise pose errors, that when propagated, leads to large errors. Thus, we distribute the rotational and translational error along the basis cycles of the graph so as to obtain a corrected pose graph. Finally, we perform a global pose optimization by minimizing the reprojection errors of the planar markers in all observed frames. The experiments conducted show that our method performs better than Structure from Motion and visual SLAM techniques.