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Abstract-A large number of applications use motion capture systems to track the location and the body posture of people. For instance, the movie industry captures actors to animate virtual characters that perform stunts. Today's tracking systems either operate with statically mounted cameras and thus can be used in confined areas only or rely on inertial sensors that allow for free and large-scale motion but suffer from drift in the pose estimate. This paper presents a novel tracking approach that aims to provide globally aligned full body posture estimates by combining a mobile robot and an inertial motion capture system. In our approach, a mobile robot equipped with a laser scanner is used to anchor the pose estimates of a person given a map of the environment. It uses a particle filter to globally localize a person wearing a motion capture suit and to robustly track the person's position. To obtain a smooth and globally aligned trajectory of the person, we solve a least squares optimization problem formulated from the motion capture suite and tracking data. Our approach has been implemented on a real robot and exhaustively tested. As the experimental evaluation shows, our system is able to provide locally precise and globally aligned estimates of the person's full body posture.

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Coating of tablets is a widely applied unit operation in the pharmaceutical industry. Thickness and uniformity of the coating layer are crucial for efficacy as well as for compliance. Not only due to different initiatives it is thus essential to monitor and control the coating process in-line. Optical coherence tomography (OCT) was already shown in previous works to be a suitable candidate for in-line monitoring of coating processes. However, to utilize the full potential of the OCT technology an automatic evaluation of the OCT measurements is essential. The automatic evaluation is currently implemented in MATLAB and includes several steps: (1) extraction of features of each A-scan, (2) classification of Ascan measurements based on their features, (3) detection of interfaces (air/coating and coating/tablet core), (4) correction of distortions due to the curvature of the bi-convex tablets and the oblique orientation of the tablets, and (5) determining the coating thickness. The algorithm is tested on OCT data acquired by moving the sensor head of the OCT system across a static tablet bed. The coating thickness variations of single tablets (i.e., intra-tablet coating variability) can additionally be analysed as OCT allows the measurement of the coating thickness on multiple displaced positions on one single tablet. Specifically, the information about those parameters emphasises the high capability of the OCT technology to improve process understanding and to assure a high product quality.