Investigations of closed source registration method of depth sensor technologies for human-robot collaboration

Productive teaming is the new form of human-robot interaction. The multimodal 3D imaging has a key role in this to gain a more comprehensive understanding of production system as well as to enable trustful collaboration from the teams. For a complete scene capture, the registration of the image modalities is required. Currently, low-cost RGB-D sensors are often used. These come with a closed source registration function. In order to have an efficient and freely available method for any sensors, we have developed a new method, called Triangle-Mesh-Rasterization-Projection (TMRP). To verify the performance of our method, we compare it with the closed-source projection function of the Azure Kinect Sensor (Microsoft). The qualitative comparison showed that both methods produce almost identical results. Minimal differences at the edges indicate that our TMRP interpolation is more accurate. With our method, a freely available open-source registration method is now available that can be applied to almost any multimodal 3D/2D image dataset and is not like the Microsoft SDK optimized for Microsoft products

Event-based sensor fusion in human-machine teaming

Realizing intelligent production systems where machines and human workers can team up seamlessly demands a yet unreached level of situational awareness. The machines' leverage to reach such awareness is to amalgamate a wide variety of sensor modalities through multisensor data fusion. A particularly promising direction to establishing human-like collaborations can be seen in the use of neuro-inspired sensing and computing technologies due to their resemblance with human cognitive processing. This note discusses the concept of integrating neuromorphic sensing modalities into classical sensor fusion frameworks by exploiting event-based fusion and filtering methods that combine time-periodic process models with event-triggered sensor data. Event-based sensor fusion hence adopts the operating principles of event-based sensors and even exhibits the ability to extract information from absent data. Thereby, it can be an enabler to harness the full information potential of the intrinsic spiking nature of event-driven sensors