Comparative Study of Visual SLAM-Based Mobile Robot Localization Using
  Fiducial Markers
Jongwon Lee Su Yeon Choi David Hanley Timothy Bretl
This paper presents a comparative study of three modes for mobile robot
localization based on visual SLAM using fiducial markers (i.e., square-shaped
artificial landmarks with a black-and-white grid pattern): SLAM, SLAM with a
prior map, and localization with a prior map. The reason for comparing the
SLAM-based approaches leveraging fiducial markers is because previous work has
shown their superior performance over feature-only methods, with less
computational burden compared to methods that use both feature and marker
detection without compromising the localization performance. The evaluation is
conducted using indoor image sequences captured with a hand-held camera
containing multiple fiducial markers in the environment. The performance
metrics include absolute trajectory error and runtime for the optimization
process per frame. In particular, for the last two modes (SLAM and localization
with a prior map), we evaluate their performances by perturbing the quality of
prior map to study the extent to which each mode is tolerant to such
perturbations. Hardware experiments show consistent trajectory error levels
across the three modes, with the localization mode exhibiting the shortest
runtime among them. Yet, with map perturbations, SLAM with a prior map
maintains performance, while localization mode degrades in both aspects.