Experimental Comparison of Fiducial Markers for Pose Estimation
Robust localization is critical for the navigation and control of mobile robots. Global Navigation Satellite Systems (GNSS), Visual Inertial Odometry (VIO), and Simultaneous Localization and Mapping (SLAM) offer different methods for achieving this goal. In some cases however, these methods may not be available or provide high enough accuracy. In such cases, these methods may be augmented or replaced with fiducial marker pose estimation. Fiducial markers can increase the accuracy and robustness of a localization system by providing an easily recognizable feature with embedded fault detection. In this project, we picked four open-source packages (ARTag, AprilTag, ArUco, and STag) that represent the state-of-the-art and most widely used packages and compared them in terms of their accuracy, detection rate and computational cost. Different marker configurations, including single markers, planar and non-planar bundles and multi-sized marker bundles were considered as well as simulated noise from shadows and motion blur. Finally, within this project, we developed and released the ROS package for STag.