088 - Proof-of-concept of advanced navigation algorithms based on factor graph optimization

088 - Proof-of-concept of advanced navigation algorithms based on factor graph optimization


Factor graph optimization is gaining attention from the community (it has won the Google challenge) 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. 

The tasks to be performed shall include: 

  • Review of the state of the art in factor graph optimization 
  • Design and development of a test bed including a factor graph navigation algorithm processor and the implementation of a reference algorithm based on KF 
  • Comparison of reference KF and factor graph implementation in different user scenarios, in particular:
    • when exploiting GNSS observables from different receiver grades (from high-end to low-quality / mass-market receivers for smartphone/IoT applications), and no data of other sensors; 
    • when applied for integrating GNSS with other sensors (different types and grades, including, but not limited, inertial sensors); 
    • In the presence of different user conditions, including harsh propagation scenarios typical of urban and indoor environments. 
  • Assess the benefits provided, based on real field measurement campaigns and observables from commercial/mass-market receivers.


The main outputs of the activity will consist of: 

  • Design of innovative positioning solutions based on factor graph optimization for different receiver grades 
  • Testbed including commercial/mass-market receivers 
  • Test report of performance comparison between Factor Graph implementation and reference KF 
  • Roadmap for commercialization, including potential NAVISP EL2 activities