Modular Multi-Sensor Fusion: A Collaborative State Estimation Perspective

Fusing information from multiple sensors in aprobabilistic framework can significantly improve the estimation of the system's states, allow for redundancy, and for adaptations in dynamic environments. However, the computation complexity increases with the number of sensors and the dimension of the estimated states. In this letter, we show that techniques used in the field of CSE for distributed estimators on decoupled agents can be applied on local estimators to decouple states from different sensors. Bridging the gap between these domains allows us to propose a novel unified modular multi-sensor fusion strategy for recursive filters that (i) achieves constant maintenance complexity in propagation and private update steps, (ii) supports any-sensor-to-any-sensor observations, (iii) isolates state propagation supporting high rates for propagation sensors, (iv) isolates private sensor updates, and (v) isolates joint updates requiring only the participating sensors' estimates all while maintaining probabilistic consistency. We port existing CSE strategies into MMSF formulations and compare them as well as a classical MMSF approach against the proposed one in terms of execution time, accuracy, and credibility on real data.