Factor graph optimization is gaining attention from the community as is outperforming traditional positioning algorithms. Factor graph optimization may be used to replace the traditional Weighted Least Square (WLS) or Kalman Filter (KF)-based solutions, both in stand-alone and hybrid positioning solutions in combination with sensors. Thus, its application may be of interest for the derivation of improved positioning solutions for different receiver grades, including low-end/low-quality mass-market receivers. 

The objective of the activity is to investigate innovative navigation algorithms exploiting factor graph optimization, both in standalone and in combination with other sensors, and for different receiver grades (including low-quality observables) and ambiguity resolution for high accuracy. The activity will benchmark both the potential accuracy improvement and the computational cost of the proposed algorithms.