# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
###### Abstract
Such surfaces pose a challenge to conventional 3D reconstruction algorithms that require the establishment of robust feature correspondences across multiple images. In a previous work [9; 10], some of these challenges have been circumvented by extracting edge information from images provided by an array of two cameras as a prior step to stereo matching. The system obtains a relatively sparse 3D representation of the scene that is, at a later stage, matched with the known 3D model of the cassette to calculate the six-DOF transformation between the camera and the target. We consider the aforementioned approach to be sub-optimal for the application, since it does not explicitly exploit the prior knowledge of the structure of the environment at the feature detection and geometry reconstruction stages. As a result, this earlier solution is not as robust or accurate as required for the application at hand. We hypothesize that more reliable results can be achieved by detecting the known features of the cassette, or, even better, through the introduction of marker objects in the cassette surface that can be tracked by cameras unambiguously and accurately. Furthermore, there is a need, due to space constraints, to reduce the number of cameras to just one while still maintaining the targeted high-accuracy target pose estimation. Marker-based tracking has been used in human motion analysis [11], medicine [12], and augmented reality [13; 14; 15] applications, localization of wearables [16], and in unmanned aerial vehicle and robot navigation systems [17; 18; 19; 20]. It has also been used in industrial contexts [11], such as component inspection and validation [21; 22]. It can obtain significantly superior results compared to tracking natural features, particularly when objects in the scene have limited contrast features, have few identifiable features, or change appearance with viewpoint [22; 23]. Marker objects used for tracking can be LEDs, white diffuse markers, light projected targets, or retro-reflective markers [22]. A retro-reflector is an optical element that returns light to its source for a wide range of incident directions. They are best known for their use in traffic safety, where they improve visibility under poor light conditions by returning incoming headlamp light in a small cone that covers the viewpoint of the driver. The intensity of retro-reflected light in the direction of the source can be up to hundreds to even thousands of times higher than that of a diffuse target [24]. They have also been used together with a laser for making distance measurements in space [25], industrial [26], and robotics applications [27]. In photogrammetry, retro-reflective markers allow the production of an almost binary image by controlling image exposure in such a way that the background is under-exposed and largely eliminated [28]. This process has advantages in target recognition and extraction, high-precision locating, and noise suppression [24]. As the above-mentioned characteristics deem retro-reflective markers optimal for the ITER application, in this paper, we aim to design a retro-reflective marker setting that complies with the strict requirements of such environment and can be attached to the surface of the cassettes for their effective tracking. We develop and test a system that uses said markers to reliably estimate the alignment between a pin-tool and the respective slots in the cassette surface. The concept has previously been presented on a system level to the fusion engineering community [29]. Here, we detail it from a machine vision perspective with the following contributions:  
* A marker design that complies with the extremely strict requirements set by the operating environment. * A methodology for marker detection and correspondence and optimization of the estimated pose. * An approach for remote calibration of radiation-activated cameras. * A study of the performance of monocular versus stereoscopic pose estimation on synthetically generated data with varying baselines.