In hospitals but also in other public facilities, it is essential to minimize the risk of contagion from infected persons. One of the key aspects is therefore to avoid contact infections caused by touching contaminated surfaces. While the current practice of wipe disinfection carried out by cleaning staff is expedient, it makes objective documentation difficult, can lead to surface damage by sanitizer overdosage, and can even put people at risk due to the released vapors. Consequently, it would be beneficial to implement technical solutions for both efficient and gentle disinfection of surfaces, e.g., a mobile platform with a sanitization module attached to a robotic arm. For a targeted cleaning and disinfection, which is tailored to specific objects and materials, such a system requires sensor technology for analyzing the environment. With this purpose in mind, we have developed a multimodal 3D sensor for detecting objects that can typically be found in a hospital environment. We started by examining specific materials using a spectrometer as well as cameras of various spectral ranges. Based on the results, we developed a sensor that can provide multimodal surface data with high spatial and temporal resolution. In experiments, we investigated how the generated data stream can be utilized for the targeted identification and treatment of typical hospital objects.
Pattern projection-based stereo 3D sensors are widely used for contactless, non-destructive optical 3D shape measurements. In previous works, we have shown 3D measurement systems based on stereo matching between two cameras with GOBO-projected aperiodic fringe patterns. We have also demonstrated a low latency 3D reconstruction algorithm (BICOS), which can be used for real time 3D measurements. We showed an optimization method for the projected aperiodic fringe patterns with the purpose of making the measurements more robust and to reduce the pattern sequence length without sacrificing 3D model completeness. In this contribution, we demonstrate a sensor for a medical application which aggregates these developments. Our sensor is used to monitor patient movement during radiation therapy. In this application a low measurement latency is of high importance. A significant part of this latency is caused by image acquisition. We show that we can reduce the number of required image pairs to 6 when optimizing the projected aperiodic fringe patterns. In combination with our BICOS algorithm, we can achieve total measurement latencies of below 80 ms at an accuracy of 355 μm in a measurement field of 1 m × 2 m.
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