The Use of Multi-Scale Fiducial Markers To Aid Takeoff and Landing Navigation by Rotorcraft

This paper quantifies the impact of adverse environmental conditions on the detection of fiducial markers (i.e., artificial landmarks) by color cameras mounted on rotorcraft. We restrict our attention to square markers with a black-and-white pattern of grid cells that can be nested to allow detection at multiple scales. These markers have the potential to enhance the reliability of precision takeoff and landing at vertiports by flying vehicles in urban settings. Prior work has shown, in particular, that these markers can be detected with high precision (i.e., few false positives) and high recall (i.e., few false negatives). However, most of this prior work has been based on image sequences collected indoors with hand-held cameras. Our work is based on image sequences collected outdoors with cameras mounted on a quadrotor during semi-autonomous takeoff and landing operations under adverse environmental conditions that include variations in temperature, illumination, wind speed, humidity, visibility, and precipitation. In addition to precision and recall, performance measures include continuity, availability, robustness, resiliency, and coverage volume. We release both our dataset and the code we used for analysis to the public as open source.

Comparative Study of Visual SLAM-Based Mobile Robot Localization Using Fiducial Markers

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.