Multimodal 3D sensor for object recognition in hospital settings

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.