2022 – Mobile robot sensor fusion

We have prepared all the necessary materials for acquisitions:

  • encoder of the two wheels
  • gyro velocity output
  • a reference system ground truth to compare your algorithm and eventually optimise parameters

Send an email to alessandro.luchetti@unitn.it to schedule a day to come to the lab to acquire the data. 

You could follow two different approaches:

  • Sensor Fusion in the statistical domain, i.e. Bayes or Kalman filtering
  • Sensor Fusion in the frequency/time domain:
    • fusing only attitude increments with 1-dimensional Complementary Filtering
    • fusing also position. In this case use quaternions