Fault-tolerant probabilistic sensor fusion for Multi-Agent Systems

In this work we focus on the problem of probabilistic sensor fusion in Multi-Robot Multi-Sensor Systems (MRMS), taking into account that some sensors might fail or produce erroneous information. We study fusion methods that can successfully cope with situations of agreement, partial agreement, and disagreement between sensors. We define a set of specifications for fusion methods appropriate for MRMS environments. In light of these specifications, we review two popular algorithms for probabilistic sensor fusion, Linear Opinion Pool (LOP) and Logarithmic Opinion Pool (LGP). To overcome difficulties of applying them to a MRMS setting, a new method is introduced, p-norm Opinion Pool (POP). Comparing to LOP and LGP, POP is more compatible with the specifications and more flexible, successfully handling situations of agreement and disagreement between sensors. Through simulation and real-world experiments, we check performance of the POP and compare it with LOP and LGP. We also implement a real-world experiment through which the performance of POP is examined.