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.